Persistent network model diagnostics - balanced statistics
This file shows diagnostics for persistent network models fit using balanced racial/ethnic mixing matrices and degree terms adjusted to correspond to the balanced mixing matrices. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
library("here")## here() starts at /homes/dpwhite/R/GitHub Repos/WHAMPload(file = here("Model fits and simulations/Fit tests and debugging/est/fit.p.buildup.bal.rda"))Model terms and control settings
| Terms | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|---|---|---|---|
| edges | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 |
| nodefactor.deg.main.1 | NA | NA | NA | 1699.0 | 1699.0 | 1699.0 | 1699.0 | 1699.0 |
| nodefactor.race..wa.B | NA | 285.5 | 285.5 | 285.5 | 285.5 | 285.5 | 285.5 | 285.5 |
| nodefactor.race..wa.H | NA | 605.3 | 605.3 | 605.3 | 605.3 | 605.3 | 605.3 | 605.3 |
| nodefactor.region.EW | NA | NA | NA | NA | 367.6 | 367.6 | 367.6 | 367.6 |
| nodefactor.region.OW | NA | NA | NA | NA | 1182.3 | 1182.3 | 1182.3 | 1182.3 |
| concurrent | NA | NA | NA | NA | NA | NA | 1384.0 | 1384.0 |
| nodematch.race..wa.B | NA | NA | 8.5 | 8.5 | 8.5 | 8.5 | 8.5 | 8.5 |
| nodematch.race..wa.H | NA | NA | 51.2 | 51.2 | 51.2 | 51.2 | 51.2 | 51.2 |
| nodematch.race..wa.O | NA | NA | 1247.1 | 1247.1 | 1247.1 | 1247.1 | 1247.1 | 1247.1 |
| nodematch.region | NA | NA | NA | NA | NA | NA | NA | 1614.0 |
| absdiff.sqrt.age | NA | NA | NA | NA | NA | 1664.8 | 1664.8 | 1664.8 |
| degrange | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| nodematch.role.class.I | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
| nodematch.role.class.R | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
The control settings for these models are:
set.control.ergm = ccontrol.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
init.method = "zeros",
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
Model 1
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## -0.2693 40.4159 0.2333 0.2324
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## -78.5 -27.5 -0.5 26.5 78.5
##
##
## Sample statistics cross-correlations:
## edges
## edges 1
##
## Sample statistics auto-correlation:
## Chain 1
## edges
## Lag 0 1.00000000
## Lag 1e+05 -0.01264310
## Lag 2e+05 0.02340828
## Lag 3e+05 -0.00426963
## Lag 4e+05 -0.01833374
## Lag 5e+05 -0.02891705
## Chain 2
## edges
## Lag 0 1.0000000000
## Lag 1e+05 0.0097286168
## Lag 2e+05 -0.0004894332
## Lag 3e+05 0.0142298259
## Lag 4e+05 0.0077479447
## Lag 5e+05 0.0153968890
## Chain 3
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.017667766
## Lag 2e+05 0.013800249
## Lag 3e+05 0.005901856
## Lag 4e+05 0.002580839
## Lag 5e+05 0.003736537
## Chain 4
## edges
## Lag 0 1.00000000
## Lag 1e+05 0.01953609
## Lag 2e+05 0.01789521
## Lag 3e+05 -0.01244918
## Lag 4e+05 -0.02167088
## Lag 5e+05 0.01275054
## Chain 5
## edges
## Lag 0 1.0000000000
## Lag 1e+05 -0.0055063925
## Lag 2e+05 0.0083421464
## Lag 3e+05 -0.0131939117
## Lag 4e+05 -0.0001138946
## Lag 5e+05 0.0158752099
## Chain 6
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.018427988
## Lag 2e+05 0.008982739
## Lag 3e+05 -0.030548372
## Lag 4e+05 0.018199035
## Lag 5e+05 -0.006587675
## Chain 7
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.034409587
## Lag 2e+05 0.017008519
## Lag 3e+05 -0.016041825
## Lag 4e+05 -0.006265774
## Lag 5e+05 0.014039680
## Chain 8
## edges
## Lag 0 1.00000000
## Lag 1e+05 0.01156856
## Lag 2e+05 0.02465699
## Lag 3e+05 0.01027490
## Lag 4e+05 -0.01866472
## Lag 5e+05 0.00814208
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.2032
##
## Individual P-values (lower = worse):
## edges
## 0.8389906
## Joint P-value (lower = worse): 0.8345032 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.4927
##
## Individual P-values (lower = worse):
## edges
## 0.6222378
## Joint P-value (lower = worse): 0.6365891 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.3314
##
## Individual P-values (lower = worse):
## edges
## 0.7403632
## Joint P-value (lower = worse): 0.7407062 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.0489
##
## Individual P-values (lower = worse):
## edges
## 0.9609959
## Joint P-value (lower = worse): 0.9610362 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 1.481
##
## Individual P-values (lower = worse):
## edges
## 0.1385718
## Joint P-value (lower = worse): 0.1321135 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 1.746
##
## Individual P-values (lower = worse):
## edges
## 0.0808636
## Joint P-value (lower = worse): 0.08687598 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.6178
##
## Individual P-values (lower = worse):
## edges
## 0.5367057
## Joint P-value (lower = worse): 0.511578 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.1803
##
## Individual P-values (lower = worse):
## edges
## 0.8569124
## Joint P-value (lower = worse): 0.8625581 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 2
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.09110 39.98 0.23084 0.22820
## nodefactor.race..wa.B 0.07903 16.15 0.09322 0.09365
## nodefactor.race..wa.H 0.68867 23.45 0.13537 0.13636
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -78.50 -26.50 -0.5000 26.50 78.50
## nodefactor.race..wa.B -31.52 -10.52 0.4832 10.48 32.48
## nodefactor.race..wa.H -44.34 -15.34 0.6600 16.66 46.66
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.34043850
## nodefactor.race..wa.B 0.3404385 1.00000000
## nodefactor.race..wa.H 0.4714240 0.07104756
## nodefactor.race..wa.H
## edges 0.47142397
## nodefactor.race..wa.B 0.07104756
## nodefactor.race..wa.H 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.014056138 0.002684782 -0.009837923
## Lag 2e+05 0.009601482 0.027872754 -0.006658632
## Lag 3e+05 -0.021384550 0.016004081 -0.003078447
## Lag 4e+05 0.020746657 0.011123528 0.018909300
## Lag 5e+05 -0.031688277 0.021454748 0.006354803
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.015375920 0.013907793 0.005285046
## Lag 2e+05 0.004422912 0.010542225 -0.010488125
## Lag 3e+05 0.008273100 -0.021453347 -0.003996662
## Lag 4e+05 -0.020135105 0.014140349 -0.018196243
## Lag 5e+05 0.016189235 -0.009558596 0.002314518
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.008418285 0.020811394 -0.02164845
## Lag 2e+05 0.007272971 0.014815020 -0.01199940
## Lag 3e+05 -0.011430331 0.006676682 0.02394959
## Lag 4e+05 0.013043680 -0.026303588 0.01219512
## Lag 5e+05 -0.008226237 -0.019227916 -0.02001891
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.001047007 0.007165719 0.004493936
## Lag 2e+05 -0.002856752 -0.004022528 0.011372678
## Lag 3e+05 -0.004367930 0.016923844 -0.012796269
## Lag 4e+05 -0.015947214 -0.004892327 0.001331095
## Lag 5e+05 -0.019503067 -0.016475032 0.022703284
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.007100868 0.036214870 0.006731455
## Lag 2e+05 0.023051907 0.013640057 0.019028848
## Lag 3e+05 -0.016022507 -0.002459983 -0.017163470
## Lag 4e+05 -0.003299190 0.019133762 -0.026224077
## Lag 5e+05 0.008201155 -0.015749485 0.040439013
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.008263184 0.001980644 0.010036553
## Lag 2e+05 -0.008049266 -0.014203663 0.019661007
## Lag 3e+05 0.002443602 0.008704505 0.016815173
## Lag 4e+05 -0.036731424 -0.011608567 0.001098624
## Lag 5e+05 0.021488107 -0.006153972 0.007991003
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.004780772 0.006810960 -0.016046380
## Lag 2e+05 -0.001116000 -0.004969817 -0.012715600
## Lag 3e+05 -0.006197987 -0.019398210 0.002192296
## Lag 4e+05 -0.027419983 -0.002447443 -0.007998861
## Lag 5e+05 0.005475253 0.005611519 -0.004733637
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.025949984 0.020703104 -0.004187630
## Lag 2e+05 -0.009835624 0.010510133 -0.002304295
## Lag 3e+05 -0.006874789 0.004327962 -0.002507147
## Lag 4e+05 -0.021181794 0.009414561 -0.036419091
## Lag 5e+05 0.033435427 0.041390937 0.014618093
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.6982 -0.0121 -0.9571
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4850279 0.9903456 0.3385000
## Joint P-value (lower = worse): 0.790269 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.0060 0.8227 -0.2323
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3143990 0.4106945 0.8162875
## Joint P-value (lower = worse): 0.6425545 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.7328 -0.6441 -0.3608
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4636517 0.5194926 0.7182590
## Joint P-value (lower = worse): 0.8784429 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.03504 0.79803 -0.75937
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9720451 0.4248510 0.4476300
## Joint P-value (lower = worse): 0.7355735 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.07216 -0.62653 -0.43441
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9424724 0.5309689 0.6639910
## Joint P-value (lower = worse): 0.8647927 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4155 0.6445 0.2857
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6777416 0.5192281 0.7751256
## Joint P-value (lower = worse): 0.929725 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.2834 0.1738 -1.6865
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.19936280 0.86201320 0.09170284
## Joint P-value (lower = worse): 0.2793509 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -2.6602 -0.8513 -1.4327
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.007810195 0.394610098 0.151944926
## Joint P-value (lower = worse): 0.08011385 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 3
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.49160 40.392 0.23320 0.22875
## nodefactor.race..wa.B 0.10247 16.100 0.09295 0.09439
## nodefactor.race..wa.H -0.64150 23.741 0.13707 0.13574
## nodematch.race..wa.B 0.03895 2.903 0.01676 0.01672
## nodematch.race..wa.H -0.08816 6.900 0.03984 0.03983
## nodematch.race..wa.O -0.01568 32.784 0.18928 0.18931
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -78.50 -27.500 -0.50000 26.500 79.50
## nodefactor.race..wa.B -30.52 -10.517 -0.51680 10.483 32.48
## nodefactor.race..wa.H -46.34 -17.340 -0.34000 15.660 45.66
## nodematch.race..wa.B -5.48 -2.480 -0.47985 1.520 6.52
## nodematch.race..wa.H -13.18 -5.181 -0.18150 4.819 13.82
## nodematch.race..wa.O -63.08 -22.081 -0.08078 21.919 64.92
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.35032349
## nodefactor.race..wa.B 0.3503235 1.00000000
## nodefactor.race..wa.H 0.4731012 0.10951385
## nodematch.race..wa.B 0.0510416 0.31164161
## nodematch.race..wa.H 0.1251259 -0.01310216
## nodematch.race..wa.O 0.7840966 -0.01465899
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.47310122 0.051041602
## nodefactor.race..wa.B 0.10951385 0.311641607
## nodefactor.race..wa.H 1.00000000 -0.021040693
## nodematch.race..wa.B -0.02104069 1.000000000
## nodematch.race..wa.H 0.49246973 -0.005351267
## nodematch.race..wa.O -0.03079702 0.007037903
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.125125858 0.784096612
## nodefactor.race..wa.B -0.013102161 -0.014658986
## nodefactor.race..wa.H 0.492469731 -0.030797024
## nodematch.race..wa.B -0.005351267 0.007037903
## nodematch.race..wa.H 1.000000000 0.004971174
## nodematch.race..wa.O 0.004971174 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.004194474 0.0075914377 -0.012642645
## Lag 2e+05 0.001189938 -0.0077760006 -0.013051100
## Lag 3e+05 -0.013498475 -0.0015569134 -0.009593246
## Lag 4e+05 -0.028407617 -0.0002640455 -0.004609579
## Lag 5e+05 0.023342365 0.0074494724 0.020201513
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.017936373 -0.004130326 -0.001240103
## Lag 2e+05 -0.011342263 -0.007115653 0.011922060
## Lag 3e+05 0.008160085 -0.028034668 0.004731922
## Lag 4e+05 0.003387611 0.013319112 -0.008112927
## Lag 5e+05 0.000192203 0.001983806 0.035126530
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.0155963242 0.005599207 -0.0065676747
## Lag 2e+05 -0.0056243883 0.007062517 0.0020552492
## Lag 3e+05 -0.0033827699 -0.005364336 -0.0172668074
## Lag 4e+05 -0.0222674387 0.030549916 0.0005323911
## Lag 5e+05 0.0007441259 -0.005040395 0.0069328232
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 -0.01325389 0.021283659 -0.002475173
## Lag 2e+05 0.01487761 0.022805977 -0.013027309
## Lag 3e+05 0.02235377 -0.006504199 0.027756411
## Lag 4e+05 -0.04650066 -0.005498602 -0.013712560
## Lag 5e+05 0.01115581 -0.002193962 -0.004388494
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.0000000000 1.00000000
## Lag 1e+05 0.0011042129 -0.0034361775 -0.02295850
## Lag 2e+05 0.0003558665 -0.0135148853 0.01457583
## Lag 3e+05 0.0257859960 0.0067996675 -0.03581431
## Lag 4e+05 0.0221185423 0.0002173176 0.02190516
## Lag 5e+05 -0.0159792426 0.0067314282 -0.03160936
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.0000000000 1.000000000
## Lag 1e+05 0.00184724 -0.0154134135 -0.012907295
## Lag 2e+05 -0.01540586 0.0055993066 0.002586412
## Lag 3e+05 0.02086939 -0.0102281941 0.011847935
## Lag 4e+05 -0.01260758 -0.0037541334 -0.008167550
## Lag 5e+05 -0.01998848 0.0005672827 -0.004071502
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000e+00 1.000000000
## Lag 1e+05 0.0224645616 1.784195e-02 0.010455881
## Lag 2e+05 0.0089836522 4.040036e-02 -0.008024200
## Lag 3e+05 -0.0141784233 -8.261718e-05 -0.003862752
## Lag 4e+05 -0.0091492865 2.992907e-02 0.003487751
## Lag 5e+05 0.0002028272 -3.524313e-03 -0.001226870
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.016053619 0.016979932 0.004451795
## Lag 2e+05 -0.028064110 -0.014351816 0.014273858
## Lag 3e+05 0.002085090 -0.003588906 -0.004309958
## Lag 4e+05 -0.001016541 -0.010506408 0.004567370
## Lag 5e+05 -0.028698303 -0.040388134 0.026708334
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.007854902 0.011695396 -0.012191540
## Lag 2e+05 -0.005259711 -0.013199314 -0.000860708
## Lag 3e+05 0.023212640 0.020899059 0.001762955
## Lag 4e+05 0.034799520 0.039169651 0.003499383
## Lag 5e+05 0.008316642 -0.005131585 0.041519582
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.019129467 0.013971358 0.014335328
## Lag 2e+05 -0.002905375 0.009345281 -0.011161619
## Lag 3e+05 0.013839668 0.017660618 -0.005213653
## Lag 4e+05 0.022730103 0.018097662 0.010710719
## Lag 5e+05 -0.015218436 0.002986982 0.008519737
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.0081564273 0.0087633288 0.0070437959
## Lag 2e+05 0.0007834107 -0.0067433259 0.0149598041
## Lag 3e+05 -0.0134839361 0.0081827626 0.0006985165
## Lag 4e+05 -0.0067049282 -0.0009645366 -0.0017836691
## Lag 5e+05 -0.0152227996 -0.0036686520 -0.0209157048
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.007473357 -0.013586701 0.002125045
## Lag 2e+05 -0.003967014 -0.010837192 0.011437851
## Lag 3e+05 -0.004074421 0.001415328 -0.007356995
## Lag 4e+05 0.026260508 -0.005507372 -0.003251000
## Lag 5e+05 -0.028163935 0.008707329 -0.021063262
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.033540290 -0.0153248465 -0.009173664
## Lag 2e+05 -0.016990173 -0.0008163169 0.006012257
## Lag 3e+05 0.001463528 -0.0202440828 0.017539643
## Lag 4e+05 0.008396229 -0.0160918846 -0.016798487
## Lag 5e+05 0.003859869 -0.0208920113 -0.024695040
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.01063046 0.029248963 -0.009934672
## Lag 2e+05 -0.01162834 0.008072418 -0.018618860
## Lag 3e+05 0.01196546 0.037372123 0.002967483
## Lag 4e+05 0.01578000 -0.030478822 0.001248421
## Lag 5e+05 0.01399681 -0.012613542 0.001928380
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.012470353 0.003658270 -0.0277476196
## Lag 2e+05 -0.008046442 0.009763684 -0.0008693885
## Lag 3e+05 -0.022457371 -0.005274708 -0.0299199312
## Lag 4e+05 -0.018725001 0.008761441 0.0114885925
## Lag 5e+05 0.005299452 -0.009391127 0.0197180266
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.012070816 -0.01414538 0.024201125
## Lag 2e+05 -0.018249011 0.01110926 -0.023028787
## Lag 3e+05 0.017811469 -0.02073407 -0.010077203
## Lag 4e+05 0.014853740 0.01365676 0.011524809
## Lag 5e+05 -0.009943323 -0.01517775 0.003422765
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.4498 0.2695 1.0898
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1977 -0.2121 -1.1982
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6528754 0.7875718 0.2758045
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8433102 0.8320154 0.2308482
## Joint P-value (lower = worse): 0.7042222 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.84024 -1.12084 -0.60216
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.07758 0.02077 -0.37963
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4007743 0.2623556 0.5470695
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.9381661 0.9834285 0.7042179
## Joint P-value (lower = worse): 0.8389077 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4961 -0.6390 0.2576
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1190 -0.5697 0.5317
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6198235 0.5228062 0.7967199
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.9052781 0.5689107 0.5949412
## Joint P-value (lower = worse): 0.9484161 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.6077 1.5640 0.4276
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.5840 1.7052 1.5777
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.10789969 0.11782802 0.66894872
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.55925236 0.08815563 0.11462551
## Joint P-value (lower = worse): 0.1424581 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.7565 -0.1949 1.8757
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.0085 -0.4302 1.1165
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.07900789 0.84547797 0.06069433
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.31320179 0.66703828 0.26418861
## Joint P-value (lower = worse): 0.1941955 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.2579 0.7303 -1.0786
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.2551 -0.7524 -0.1256
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7965156 0.4651883 0.2807608
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.7986409 0.4518302 0.9000303
## Joint P-value (lower = worse): 0.9569326 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4566 -0.4246 -0.7287
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.1565 -0.5067 1.4818
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6479364 0.6711539 0.4662087
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8756597 0.6123817 0.1384026
## Joint P-value (lower = worse): 0.5309442 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.55076 -0.05389 -0.29861
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.18073 -1.14701 0.59100
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5817984 0.9570241 0.7652376
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8565803 0.2513762 0.5545174
## Joint P-value (lower = worse): 0.8805751 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 4
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.0003667 40.088 0.23145 0.23263
## nodefactor.deg.main.1 -0.0795333 45.078 0.26026 0.25871
## nodefactor.race..wa.B 0.6763000 16.041 0.09262 0.09394
## nodefactor.race..wa.H -0.4632000 23.583 0.13615 0.13844
## nodematch.race..wa.B 0.0135177 2.880 0.01663 0.01671
## nodematch.race..wa.H -0.1525637 6.927 0.03999 0.03975
## nodematch.race..wa.O -0.4243794 32.677 0.18866 0.18700
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -78.50 -26.500 -0.5000 26.500 79.50
## nodefactor.deg.main.1 -89.00 -30.000 0.0000 30.000 89.00
## nodefactor.race..wa.B -30.52 -10.517 0.4832 11.483 32.48
## nodefactor.race..wa.H -46.34 -16.340 -0.3400 15.660 46.66
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 6.52
## nodematch.race..wa.H -13.18 -5.181 -0.1815 4.819 13.82
## nodematch.race..wa.O -64.08 -23.081 -1.0808 21.919 63.92
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.75567574
## nodefactor.deg.main.1 0.75567574 1.00000000
## nodefactor.race..wa.B 0.34878341 0.23595585
## nodefactor.race..wa.H 0.46408333 0.39925610
## nodematch.race..wa.B 0.05407078 0.02733345
## nodematch.race..wa.H 0.12069718 0.11931936
## nodematch.race..wa.O 0.78474192 0.57816856
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.348783411 0.464083328
## nodefactor.deg.main.1 0.235955847 0.399256102
## nodefactor.race..wa.B 1.000000000 0.106971114
## nodefactor.race..wa.H 0.106971114 1.000000000
## nodematch.race..wa.B 0.300638176 -0.001563697
## nodematch.race..wa.H -0.008770457 0.498985173
## nodematch.race..wa.O -0.018362660 -0.037391781
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.054070777 0.120697180
## nodefactor.deg.main.1 0.027333453 0.119319361
## nodefactor.race..wa.B 0.300638176 -0.008770457
## nodefactor.race..wa.H -0.001563697 0.498985173
## nodematch.race..wa.B 1.000000000 0.007093001
## nodematch.race..wa.H 0.007093001 1.000000000
## nodematch.race..wa.O 0.004282978 -0.001179403
## nodematch.race..wa.O
## edges 0.784741922
## nodefactor.deg.main.1 0.578168564
## nodefactor.race..wa.B -0.018362660
## nodefactor.race..wa.H -0.037391781
## nodematch.race..wa.B 0.004282978
## nodematch.race..wa.H -0.001179403
## nodematch.race..wa.O 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.020346379 0.007257429 0.0023991539
## Lag 2e+05 0.008336525 0.008624913 0.0264586129
## Lag 3e+05 -0.008185406 -0.034032252 -0.0005977681
## Lag 4e+05 -0.016419040 -0.032652433 0.0276314928
## Lag 5e+05 -0.007842153 -0.002968748 0.0045604650
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.005322937 0.003121216 -0.021671753
## Lag 2e+05 0.007151695 0.014134361 -0.010609441
## Lag 3e+05 -0.024763491 -0.007253842 -0.003569679
## Lag 4e+05 -0.007066556 -0.011904789 0.017600048
## Lag 5e+05 0.039670993 0.021367959 0.031751262
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0092642715
## Lag 2e+05 -0.0008065677
## Lag 3e+05 0.0016950608
## Lag 4e+05 -0.0248122777
## Lag 5e+05 -0.0256992975
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.033432452 -0.021140204 -0.001643601
## Lag 2e+05 -0.009539155 -0.002464447 0.011168133
## Lag 3e+05 0.039421042 0.025503390 -0.016759099
## Lag 4e+05 -0.005227752 -0.003702030 0.004159357
## Lag 5e+05 -0.013595720 0.015074988 -0.002094999
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.006490441 -0.010859749 0.014018834
## Lag 2e+05 0.005612046 -0.010218864 -0.041821296
## Lag 3e+05 0.014641463 -0.038114557 0.002095162
## Lag 4e+05 -0.017766035 0.027919064 -0.005688775
## Lag 5e+05 -0.019271565 -0.005792261 -0.017025754
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 -0.0118583201
## Lag 2e+05 -0.0321352592
## Lag 3e+05 0.0181139106
## Lag 4e+05 -0.0269303725
## Lag 5e+05 -0.0001396649
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.008358189 -0.019641560 0.002205620
## Lag 2e+05 -0.009429479 -0.015899415 -0.019319928
## Lag 3e+05 0.020139587 0.015697208 0.005260236
## Lag 4e+05 -0.003190494 0.003094338 0.011448050
## Lag 5e+05 0.003302542 0.030350441 0.003683434
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.022040551 0.016087606 -0.009162634
## Lag 2e+05 -0.017510889 -0.013538269 0.002901417
## Lag 3e+05 -0.004109120 0.041005952 -0.005402268
## Lag 4e+05 -0.023275506 0.007468984 -0.005360512
## Lag 5e+05 -0.004071408 0.002865810 -0.018305342
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.005905203
## Lag 2e+05 0.006827892
## Lag 3e+05 0.026442015
## Lag 4e+05 0.006701167
## Lag 5e+05 -0.009486472
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.009659987 0.01079896 -0.005904286
## Lag 2e+05 0.004266297 -0.01140462 0.043051041
## Lag 3e+05 0.013721497 0.01611263 -0.007483191
## Lag 4e+05 -0.001237171 0.01227068 0.032858755
## Lag 5e+05 -0.001599683 0.01825667 0.020195799
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0065691335 0.011446100 0.010205913
## Lag 2e+05 0.0032697396 -0.025824317 -0.034037605
## Lag 3e+05 -0.0007877063 0.005548393 0.007604281
## Lag 4e+05 -0.0083929417 0.019109810 0.006101996
## Lag 5e+05 -0.0042518365 0.027201148 -0.009351544
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.004368116
## Lag 2e+05 0.015074423
## Lag 3e+05 0.011569052
## Lag 4e+05 -0.004801247
## Lag 5e+05 0.008002505
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0128824505 0.004968885 0.020246188
## Lag 2e+05 -0.0008600489 0.006758939 -0.003393436
## Lag 3e+05 -0.0038571760 0.005820086 0.011088105
## Lag 4e+05 -0.0096543200 -0.004416776 -0.001946214
## Lag 5e+05 0.0037368822 -0.003221812 0.011761987
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.004322357 0.01505552 -0.023040228
## Lag 2e+05 -0.007859073 -0.01688641 0.005403930
## Lag 3e+05 -0.008792907 -0.01648602 -0.010315436
## Lag 4e+05 0.003951401 -0.01513773 -0.005091850
## Lag 5e+05 0.011894261 -0.02705870 0.003799587
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.024408207
## Lag 2e+05 -0.005702051
## Lag 3e+05 -0.015572724
## Lag 4e+05 -0.013748425
## Lag 5e+05 0.002397250
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.023450038 -0.020089147 0.0116543781
## Lag 2e+05 -0.001952285 -0.011631077 0.0058145025
## Lag 3e+05 0.037828921 0.011849570 -0.0008881523
## Lag 4e+05 0.000463904 0.007704660 0.0088855258
## Lag 5e+05 0.015062892 -0.004875415 0.0109068840
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.004107895 0.0002986658 0.004131773
## Lag 2e+05 0.007878321 0.0520089165 0.006513362
## Lag 3e+05 -0.012233228 0.0083847460 -0.041820513
## Lag 4e+05 0.011467523 -0.0036065884 -0.005123006
## Lag 5e+05 0.023830435 0.0020294657 0.030109413
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.020415890
## Lag 2e+05 0.002197100
## Lag 3e+05 0.024719054
## Lag 4e+05 -0.001904416
## Lag 5e+05 0.008128940
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 -0.002390172 6.969027e-03 -0.013287328
## Lag 2e+05 -0.016189638 -1.287775e-05 0.012327880
## Lag 3e+05 0.006999612 1.264744e-03 -0.013569386
## Lag 4e+05 0.001622088 -1.406119e-02 -0.030649081
## Lag 5e+05 -0.010399368 -1.313095e-02 0.006087377
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.014046068 0.009281620 0.02900322
## Lag 2e+05 -0.007225317 0.029520009 -0.01475909
## Lag 3e+05 -0.031780039 0.024476791 -0.02320434
## Lag 4e+05 0.013664691 -0.002804322 -0.01425053
## Lag 5e+05 -0.008628453 -0.018140815 -0.02639694
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.003875352
## Lag 2e+05 -0.034757764
## Lag 3e+05 0.004239766
## Lag 4e+05 0.017164535
## Lag 5e+05 -0.016174285
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.03194833 0.008662342 0.05169358
## Lag 2e+05 0.01642757 0.005724186 -0.02332861
## Lag 3e+05 -0.01188635 -0.018220038 0.01870458
## Lag 4e+05 0.01574487 0.033037564 -0.01017263
## Lag 5e+05 0.01596508 0.006968348 0.03322637
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.002491915 -0.005158260 0.0157177324
## Lag 2e+05 0.004433865 -0.020937794 0.0005948879
## Lag 3e+05 0.009709178 0.013374941 -0.0004356022
## Lag 4e+05 -0.002578136 0.009232686 0.0159932375
## Lag 5e+05 0.021716248 0.019420266 0.0064974817
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0006988953
## Lag 2e+05 0.0266633979
## Lag 3e+05 -0.0192785315
## Lag 4e+05 0.0181656266
## Lag 5e+05 -0.0147040867
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.32937 1.17666 0.91220
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -0.04825 0.56778 0.90087
## nodematch.race..wa.O
## 0.17095
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7418739 0.2393302 0.3616659
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.9615144 0.5701828 0.3676576
## nodematch.race..wa.O
## 0.8642615
## Joint P-value (lower = worse): 0.860803 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.61479 0.53606 0.33620
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -1.12447 -0.33337 -0.03493
## nodematch.race..wa.O
## 1.37407
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5386930 0.5919155 0.7367237
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.2608124 0.7388525 0.9721342
## nodematch.race..wa.O
## 0.1694192
## Joint P-value (lower = worse): 0.7933805 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3535 1.3636 -1.9832
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -1.2037 -2.1218 0.4305
## nodematch.race..wa.O
## 1.3037
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.72372673 0.17268046 0.04734771
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.22870741 0.03385666 0.66685409
## nodematch.race..wa.O
## 0.19231989
## Joint P-value (lower = worse): 0.03873953 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3656 -0.8904 0.6018
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -0.2599 1.4363 0.6860
## nodematch.race..wa.O
## -0.5614
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7146980 0.3732331 0.5473029
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.7949034 0.1509087 0.4927078
## nodematch.race..wa.O
## 0.5745022
## Joint P-value (lower = worse): 0.5760181 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.2076 -1.2937 0.2086
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 1.3264 -0.4162 0.6691
## nodematch.race..wa.O
## -0.8358
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8355108 0.1957532 0.8347363
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.1847174 0.6772757 0.5034156
## nodematch.race..wa.O
## 0.4032946
## Joint P-value (lower = worse): 0.5798393 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9140 0.3557 0.8174
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 1.1499 -0.3872 1.0626
## nodematch.race..wa.O
## 0.0714
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3606940 0.7220846 0.4137030
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.2501857 0.6985740 0.2879734
## nodematch.race..wa.O
## 0.9430815
## Joint P-value (lower = worse): 0.8740967 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.4195 0.1408 -0.5261
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.3835 0.4382 1.8054
## nodematch.race..wa.O
## -0.3776
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.67484698 0.88802261 0.59880903
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.70132077 0.66125704 0.07101767
## nodematch.race..wa.O
## 0.70573529
## Joint P-value (lower = worse): 0.6427369 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.9586 -0.1157 -1.4526
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -0.2728 2.0765 0.1456
## nodematch.race..wa.O
## -0.1517
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.33774196 0.90791266 0.14632866
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.78503908 0.03784736 0.88423640
## nodematch.race..wa.O
## 0.87943263
## Joint P-value (lower = worse): 0.1592844 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 5
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.47593 40.071 0.23135 0.23067
## nodefactor.deg.main.1 -0.78657 45.209 0.26102 0.26141
## nodefactor.race..wa.B -0.32920 15.925 0.09194 0.09195
## nodefactor.race..wa.H -1.50490 23.465 0.13548 0.13432
## nodefactor.region.EW -0.40953 18.781 0.10843 0.10754
## nodefactor.region.OW 0.88057 36.695 0.21186 0.21317
## nodematch.race..wa.B -0.04772 2.880 0.01663 0.01659
## nodematch.race..wa.H -0.51176 6.864 0.03963 0.03921
## nodematch.race..wa.O 1.68085 32.657 0.18854 0.18792
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -77.50 -26.500 0.5000 27.500 78.50
## nodefactor.deg.main.1 -89.00 -31.000 -1.0000 29.000 89.00
## nodefactor.race..wa.B -31.52 -11.517 -0.5168 10.483 30.48
## nodefactor.race..wa.H -47.34 -17.340 -1.3400 13.660 45.66
## nodefactor.region.EW -36.59 -13.588 -0.5885 12.412 36.41
## nodefactor.region.OW -71.25 -24.255 0.7450 25.745 73.75
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 5.52
## nodematch.race..wa.H -13.18 -5.181 -1.1815 3.819 13.82
## nodematch.race..wa.O -62.08 -20.081 1.9192 23.919 65.92
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.75666469
## nodefactor.deg.main.1 0.75666469 1.00000000
## nodefactor.race..wa.B 0.34538833 0.23435368
## nodefactor.race..wa.H 0.46236378 0.39612780
## nodefactor.region.EW 0.38877954 0.30171174
## nodefactor.region.OW 0.66076311 0.45482557
## nodematch.race..wa.B 0.05426938 0.03365535
## nodematch.race..wa.H 0.11580470 0.11210820
## nodematch.race..wa.O 0.78819998 0.58094356
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.34538833 0.46236378
## nodefactor.deg.main.1 0.23435368 0.39612780
## nodefactor.race..wa.B 1.00000000 0.09885660
## nodefactor.race..wa.H 0.09885660 1.00000000
## nodefactor.region.EW 0.08981442 0.28433482
## nodefactor.region.OW 0.20818208 0.29471141
## nodematch.race..wa.B 0.30898892 -0.01268684
## nodematch.race..wa.H -0.02744403 0.49717804
## nodematch.race..wa.O -0.01560240 -0.03391010
## nodefactor.region.EW nodefactor.region.OW
## edges 0.388779539 0.66076311
## nodefactor.deg.main.1 0.301711740 0.45482557
## nodefactor.race..wa.B 0.089814422 0.20818208
## nodefactor.race..wa.H 0.284334822 0.29471141
## nodefactor.region.EW 1.000000000 0.11313942
## nodefactor.region.OW 0.113139422 1.00000000
## nodematch.race..wa.B 0.001161155 0.03191665
## nodematch.race..wa.H 0.104108353 0.07681344
## nodematch.race..wa.O 0.265857231 0.53468551
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.054269383 0.1158046992
## nodefactor.deg.main.1 0.033655353 0.1121082022
## nodefactor.race..wa.B 0.308988918 -0.0274440287
## nodefactor.race..wa.H -0.012686836 0.4971780368
## nodefactor.region.EW 0.001161155 0.1041083533
## nodefactor.region.OW 0.031916654 0.0768134422
## nodematch.race..wa.B 1.000000000 -0.0096487696
## nodematch.race..wa.H -0.009648770 1.0000000000
## nodematch.race..wa.O 0.004910681 0.0006486809
## nodematch.race..wa.O
## edges 0.7881999818
## nodefactor.deg.main.1 0.5809435560
## nodefactor.race..wa.B -0.0156024023
## nodefactor.race..wa.H -0.0339101030
## nodefactor.region.EW 0.2658572311
## nodefactor.region.OW 0.5346855062
## nodematch.race..wa.B 0.0049106809
## nodematch.race..wa.H 0.0006486809
## nodematch.race..wa.O 1.0000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.015306435 -0.018052938 0.00132814
## Lag 2e+05 -0.002918860 0.025922884 -0.01506075
## Lag 3e+05 0.004425414 -0.005903431 0.01340845
## Lag 4e+05 0.029138614 0.010364324 0.01215767
## Lag 5e+05 -0.025651556 -0.009713273 -0.02455062
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.022348759 -0.012341794 0.005262533
## Lag 2e+05 -0.023673776 -0.030555899 0.005043605
## Lag 3e+05 0.006939928 -0.004122538 0.005170901
## Lag 4e+05 0.004714846 -0.009094313 0.012680795
## Lag 5e+05 0.005556443 -0.019156415 -0.013788248
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.017734606 0.005118715 0.020610373
## Lag 2e+05 0.006912085 0.002286716 -0.006841280
## Lag 3e+05 0.022474364 -0.031339122 0.001099243
## Lag 4e+05 -0.011148104 0.008197210 0.032459904
## Lag 5e+05 -0.012516422 0.007180503 -0.010381251
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0131101818 0.018947703 0.020371391
## Lag 2e+05 -0.0017693739 -0.008455363 0.002862859
## Lag 3e+05 0.0021289481 -0.006500097 -0.018891447
## Lag 4e+05 0.0007318604 -0.012799490 0.004167525
## Lag 5e+05 -0.0127590483 -0.036307119 -0.009775620
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.008126948 0.015852547 0.002965173
## Lag 2e+05 0.003764299 0.014686695 -0.012398148
## Lag 3e+05 -0.031758061 -0.008976189 -0.030125373
## Lag 4e+05 -0.003743919 0.005114454 0.008317561
## Lag 5e+05 -0.005915130 0.005938460 -0.010309619
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.0000000000
## Lag 1e+05 0.01476034 -0.023544847 -0.0127970573
## Lag 2e+05 -0.01344060 -0.020653545 0.0149560035
## Lag 3e+05 0.01122950 -0.008804326 -0.0005471229
## Lag 4e+05 0.02721754 -0.017387914 -0.0019084030
## Lag 5e+05 -0.03439105 0.004839526 -0.0024251507
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.010447328 -0.0022922647 0.016690741
## Lag 2e+05 0.002562907 0.0167040053 0.007914421
## Lag 3e+05 0.009542835 0.0108454200 0.010639006
## Lag 4e+05 -0.020837504 -0.0088594085 0.003679102
## Lag 5e+05 0.008045893 -0.0001280241 0.005148860
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.002825576 0.0182685898 -0.006363499
## Lag 2e+05 0.005749679 0.0050833991 0.013130682
## Lag 3e+05 -0.003501010 -0.0101991373 -0.013225344
## Lag 4e+05 0.015505487 0.0002853342 -0.016784753
## Lag 5e+05 0.026992074 -0.0044628958 -0.003044371
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 -0.001282273 4.758006e-03 0.005160048
## Lag 2e+05 0.005670709 -1.754309e-02 0.027880685
## Lag 3e+05 -0.030596267 -2.439940e-03 0.012349108
## Lag 4e+05 -0.010581956 2.735818e-02 -0.012393788
## Lag 5e+05 0.004535701 -4.229668e-05 -0.001762647
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.025260542 -0.026998767 -0.0203873053
## Lag 2e+05 -0.005809487 0.029807133 -0.0002485406
## Lag 3e+05 -0.026344268 0.008551758 0.0277406828
## Lag 4e+05 0.004155856 0.015435780 0.0120652261
## Lag 5e+05 -0.005781379 -0.036251018 0.0015232837
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0159506737 0.004718468 -0.008474810
## Lag 2e+05 0.0037765356 0.008062613 -0.006520565
## Lag 3e+05 -0.0019222818 0.003361787 -0.008091097
## Lag 4e+05 -0.0007515139 0.007874367 -0.005389109
## Lag 5e+05 0.0075045902 -0.006946675 -0.007486147
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 0.0263192993 -0.0309300565 -0.027798249
## Lag 2e+05 0.0125463265 0.0003658643 -0.013285681
## Lag 3e+05 -0.0247372411 0.0212922262 -0.010045939
## Lag 4e+05 0.0002955329 0.0078951133 0.006639135
## Lag 5e+05 0.0046948821 -0.0087321970 -0.006410832
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.0007767478 -0.033445718 -0.0112920446
## Lag 2e+05 0.0126253264 0.012730531 -0.0076570745
## Lag 3e+05 -0.0024801973 0.008120142 0.0179439855
## Lag 4e+05 0.0078466505 0.011846235 0.0001473282
## Lag 5e+05 0.0006099981 0.006420157 0.0125179207
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.014625641 -0.014184193 0.010341751
## Lag 2e+05 0.011924162 0.011923194 -0.015350243
## Lag 3e+05 -0.006849913 -0.036408412 -0.007563223
## Lag 4e+05 -0.004274138 0.011827097 0.010560889
## Lag 5e+05 0.006557485 -0.005417356 0.032991211
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.0048137947 -0.0004479711 -0.005999612
## Lag 2e+05 0.0133257104 -0.0209609187 0.008132494
## Lag 3e+05 -0.0099064445 -0.0476163534 -0.010294433
## Lag 4e+05 -0.0090396159 -0.0014205766 -0.001789669
## Lag 5e+05 0.0006072016 -0.0277650004 0.008619264
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.009391393 0.011945380 0.002310709
## Lag 2e+05 0.001398434 -0.010611825 0.017953613
## Lag 3e+05 -0.007346506 0.003391769 -0.008381760
## Lag 4e+05 -0.012126942 -0.021727262 0.008481824
## Lag 5e+05 -0.009122910 -0.001547922 -0.004626226
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.0139031380 0.0049132295 -0.020281250
## Lag 2e+05 0.0007152583 0.0105711430 -0.001647741
## Lag 3e+05 0.0062528211 0.0180638879 -0.004061461
## Lag 4e+05 -0.0155042732 -0.0067776650 0.002843936
## Lag 5e+05 -0.0110546559 -0.0004468045 0.007485176
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.023598350 -0.0104074430 0.0127384143
## Lag 2e+05 -0.005480389 0.0031745426 0.0009307375
## Lag 3e+05 -0.004101450 0.0039285611 0.0001273943
## Lag 4e+05 -0.005725877 0.0001523318 -0.0031507134
## Lag 5e+05 -0.037219023 -0.0009030414 -0.0098082537
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.011594643 0.0002826664 -0.013071480
## Lag 2e+05 0.012237969 -0.0178709801 -0.002065485
## Lag 3e+05 0.003610117 0.0005950716 0.018719633
## Lag 4e+05 0.013104222 0.0030829340 -0.006131741
## Lag 5e+05 -0.017952335 -0.0220786928 0.001486529
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.025017860 0.0044155118 -0.004666238
## Lag 2e+05 0.005843071 0.0008068439 0.022664306
## Lag 3e+05 -0.020877121 -0.0143439536 -0.004133951
## Lag 4e+05 0.021106151 0.0190497400 0.001038634
## Lag 5e+05 -0.019865626 -0.0028894109 -0.011822922
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.003827034 -0.007935242 0.008313818
## Lag 2e+05 -0.020531069 -0.006467763 0.010320889
## Lag 3e+05 0.003889147 0.006805101 0.001265917
## Lag 4e+05 -0.001976969 -0.006885797 -0.001340558
## Lag 5e+05 -0.007986954 -0.006658264 -0.010960895
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.010399245 -0.006682664 0.009256780
## Lag 2e+05 0.002897909 0.018185848 0.026399108
## Lag 3e+05 -0.020300118 0.004607525 0.002754848
## Lag 4e+05 -0.014557238 -0.007906497 0.018838609
## Lag 5e+05 0.008274621 -0.002117132 0.023194382
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.002216389 0.0004650981 0.0101808509
## Lag 2e+05 0.010330800 -0.0360264596 0.0374094109
## Lag 3e+05 -0.028218980 0.0466678545 0.0017403295
## Lag 4e+05 0.013598505 0.0101534953 -0.0007818516
## Lag 5e+05 -0.012772702 -0.0032311997 0.0074307124
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.018430728 0.027086471 -0.003957709
## Lag 2e+05 0.015118497 0.009490426 -0.010949490
## Lag 3e+05 0.011214195 0.013294020 -0.022170848
## Lag 4e+05 -0.006568312 -0.011633989 -0.030351381
## Lag 5e+05 -0.021679373 0.007529639 0.005215417
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.6959 0.1829 0.4877
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.7269 0.1075 -2.5487
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.6848 -1.0162 -2.1654
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.08989815 0.85484227 0.62577610
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.46726614 0.91442325 0.01081184
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.49347777 0.30955724 0.03035550
## Joint P-value (lower = worse): 0.07582541 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.007836 -0.380024 -0.238627
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.108207 1.155100 0.057517
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.074759 1.167007 0.517084
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9937481 0.7039280 0.8113950
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9138312 0.2480494 0.9541333
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.9404061 0.2432076 0.6050976
## Joint P-value (lower = worse): 0.8434678 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3436 -0.8177 -0.8565
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3646 0.4779 0.5406
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.7898 1.0666 0.7338
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7311181 0.4135347 0.3917005
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.7153757 0.6327363 0.5887490
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.4296624 0.2861633 0.4630463
## Joint P-value (lower = worse): 0.730802 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.71705 -1.00242 1.11639
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.90682 -0.04609 -0.67051
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.36265 -1.43651 -1.60100
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.08597032 0.31614142 0.26425710
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.36450386 0.96324135 0.50252986
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.17299378 0.15085836 0.10937725
## Joint P-value (lower = worse): 0.3650856 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.44027 1.55756 -0.20203
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.29757 -2.12386 -0.40313
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.08772 -0.15291 0.55270
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.65973912 0.11933876 0.83989078
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.19443475 0.03368217 0.68685593
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.93009707 0.87846572 0.58047174
## Joint P-value (lower = worse): 0.1470618 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3070 -0.5344 1.1282
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.8037 -1.7452 1.0294
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.3221 0.6083 -0.4681
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.75880940 0.59307742 0.25923623
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.42159043 0.08095277 0.30328192
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.18613691 0.54296043 0.63969356
## Joint P-value (lower = worse): 0.184885 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3606 1.1080 -0.5898
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.0073 -0.9405 0.7627
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.5817 -2.7745 0.4128
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.718405510 0.267876090 0.555349389
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.313812353 0.346954918 0.445664910
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.560740977 0.005528252 0.679788317
## Joint P-value (lower = worse): 0.02274662 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.8159 0.2994 -0.4321
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9135 -0.4469 -0.5666
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.2939 -0.3949 -1.1324
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.4145300 0.7646370 0.6656552
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3610058 0.6549203 0.5709743
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1957132 0.6929414 0.2574465
## Joint P-value (lower = worse): 0.5995688 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 6
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 3.036133 40.139 0.23174 0.22932
## nodefactor.deg.main.1 3.000567 45.354 0.26185 0.26009
## nodefactor.race..wa.B -0.087833 15.960 0.09214 0.09279
## nodefactor.race..wa.H 1.645867 23.638 0.13647 0.13636
## nodefactor.region.EW -0.012600 18.897 0.10910 0.10870
## nodefactor.region.OW 2.469367 36.366 0.20996 0.20826
## nodematch.race..wa.B 0.005751 2.890 0.01669 0.01668
## nodematch.race..wa.H 0.061336 6.949 0.04012 0.04060
## nodematch.race..wa.O 1.576854 32.785 0.18929 0.18827
## absdiff.sqrt.age 5.325223 45.279 0.26142 0.25908
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -75.50 -23.500 3.5000 29.500 81.50
## nodefactor.deg.main.1 -86.00 -28.000 3.0000 34.000 92.00
## nodefactor.race..wa.B -30.52 -10.517 -0.5168 10.483 31.48
## nodefactor.race..wa.H -44.34 -14.340 1.6600 17.660 47.66
## nodefactor.region.EW -36.59 -12.588 -0.5885 12.412 37.41
## nodefactor.region.OW -68.25 -22.255 2.7450 26.745 73.75
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 5.52
## nodematch.race..wa.H -13.18 -5.181 -0.1815 4.819 13.82
## nodematch.race..wa.O -62.08 -21.081 0.9192 23.919 64.92
## absdiff.sqrt.age -82.95 -25.513 4.9456 35.828 95.00
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.75679144
## nodefactor.deg.main.1 0.75679144 1.00000000
## nodefactor.race..wa.B 0.34088449 0.23315514
## nodefactor.race..wa.H 0.46682144 0.39489733
## nodefactor.region.EW 0.39132611 0.29605980
## nodefactor.region.OW 0.65928284 0.45763357
## nodematch.race..wa.B 0.05914573 0.04279328
## nodematch.race..wa.H 0.12844616 0.12155482
## nodematch.race..wa.O 0.78498271 0.58210417
## absdiff.sqrt.age 0.73425035 0.55529280
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.34088449 0.466821441
## nodefactor.deg.main.1 0.23315514 0.394897328
## nodefactor.race..wa.B 1.00000000 0.107193923
## nodefactor.race..wa.H 0.10719392 1.000000000
## nodefactor.region.EW 0.07791901 0.298919920
## nodefactor.region.OW 0.20214304 0.287051440
## nodematch.race..wa.B 0.31316823 -0.005901527
## nodematch.race..wa.H -0.01061830 0.502863426
## nodematch.race..wa.O -0.02617551 -0.034735421
## absdiff.sqrt.age 0.25162652 0.350603871
## nodefactor.region.EW nodefactor.region.OW
## edges 0.391326114 0.65928284
## nodefactor.deg.main.1 0.296059799 0.45763357
## nodefactor.race..wa.B 0.077919007 0.20214304
## nodefactor.race..wa.H 0.298919920 0.28705144
## nodefactor.region.EW 1.000000000 0.10990237
## nodefactor.region.OW 0.109902374 1.00000000
## nodematch.race..wa.B 0.005117035 0.03089164
## nodematch.race..wa.H 0.122424431 0.07154405
## nodematch.race..wa.O 0.265983367 0.53599269
## absdiff.sqrt.age 0.282356620 0.48660288
## nodematch.race..wa.B nodematch.race..wa.H
## edges 5.914573e-02 1.284462e-01
## nodefactor.deg.main.1 4.279328e-02 1.215548e-01
## nodefactor.race..wa.B 3.131682e-01 -1.061830e-02
## nodefactor.race..wa.H -5.901527e-03 5.028634e-01
## nodefactor.region.EW 5.117035e-03 1.224244e-01
## nodefactor.region.OW 3.089164e-02 7.154405e-02
## nodematch.race..wa.B 1.000000e+00 -1.094911e-05
## nodematch.race..wa.H -1.094911e-05 1.000000e+00
## nodematch.race..wa.O 4.653035e-03 5.624247e-03
## absdiff.sqrt.age 4.487385e-02 9.655965e-02
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.784982707 0.73425035
## nodefactor.deg.main.1 0.582104171 0.55529280
## nodefactor.race..wa.B -0.026175510 0.25162652
## nodefactor.race..wa.H -0.034735421 0.35060387
## nodefactor.region.EW 0.265983367 0.28235662
## nodefactor.region.OW 0.535992694 0.48660288
## nodematch.race..wa.B 0.004653035 0.04487385
## nodematch.race..wa.H 0.005624247 0.09655965
## nodematch.race..wa.O 1.000000000 0.57135652
## absdiff.sqrt.age 0.571356515 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.003525380 -0.005947055 -0.0138529089
## Lag 2e+05 0.002650839 -0.006208131 0.0067446142
## Lag 3e+05 0.006482064 -0.012386904 -0.0236135291
## Lag 4e+05 -0.025657403 -0.027468777 -0.0001782027
## Lag 5e+05 0.003464240 0.008215969 -0.0132382485
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.0000000000 1.000000000
## Lag 1e+05 0.01222040 0.0098701402 0.008803227
## Lag 2e+05 -0.02196102 0.0233150288 0.033912992
## Lag 3e+05 -0.01055248 0.0009157982 -0.002385281
## Lag 4e+05 -0.01108913 0.0029040056 -0.002851153
## Lag 5e+05 -0.02365266 -0.0230257971 0.019007198
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.011201420 0.038493233 0.008397704
## Lag 2e+05 0.001090390 -0.003734910 0.016578998
## Lag 3e+05 -0.002957922 0.001188365 0.027616627
## Lag 4e+05 -0.035883724 -0.013582472 0.005207448
## Lag 5e+05 0.023136778 -0.013789916 -0.006364059
## absdiff.sqrt.age
## Lag 0 1.0000000000
## Lag 1e+05 -0.0116829827
## Lag 2e+05 -0.0094194502
## Lag 3e+05 -0.0009110845
## Lag 4e+05 -0.0404870523
## Lag 5e+05 -0.0007622560
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.031919346 -0.015841994 -0.01149043
## Lag 2e+05 -0.003166948 -0.025931591 -0.01908299
## Lag 3e+05 0.031966234 0.032277641 -0.01106794
## Lag 4e+05 0.005558704 -0.010517414 -0.01146389
## Lag 5e+05 -0.018594835 -0.004398479 0.02780604
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.048422868 -0.020357805 -0.017600267
## Lag 2e+05 0.022819786 -0.003341520 0.010744515
## Lag 3e+05 0.002651218 -0.007991695 0.009588094
## Lag 4e+05 -0.012307167 -0.003331527 -0.028285092
## Lag 5e+05 0.013094326 -0.021119152 -0.015114698
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.003377120 0.009366792 -0.007388680
## Lag 2e+05 0.018403909 -0.013284202 -0.006054701
## Lag 3e+05 -0.005355753 -0.017973034 0.020217681
## Lag 4e+05 0.027839266 -0.005827875 -0.001293279
## Lag 5e+05 -0.005872895 0.035720949 -0.020722114
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.021033201
## Lag 2e+05 -0.012039030
## Lag 3e+05 0.008318685
## Lag 4e+05 0.021597542
## Lag 5e+05 -0.009519221
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.012136667 0.009915924 -0.011598561
## Lag 2e+05 -0.044755115 -0.031677419 -0.022369786
## Lag 3e+05 0.001841987 0.015403488 -0.001669568
## Lag 4e+05 -0.006624650 -0.017690808 -0.029496836
## Lag 5e+05 -0.018458553 -0.010738995 -0.008244597
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 0.006710914 -6.633948e-03 -0.036688612
## Lag 2e+05 -0.023287517 -7.356797e-03 -0.024298586
## Lag 3e+05 -0.004828836 -6.775267e-05 -0.024445962
## Lag 4e+05 0.012323691 1.332838e-02 0.006662862
## Lag 5e+05 -0.018789115 2.043752e-02 -0.026441648
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.01466441 0.032863503 -0.016471913
## Lag 2e+05 -0.01604466 0.009461590 -0.030383400
## Lag 3e+05 -0.02185478 0.014120354 -0.008890067
## Lag 4e+05 -0.01955723 0.013455177 0.006159624
## Lag 5e+05 -0.01108289 -0.004100987 0.004704283
## absdiff.sqrt.age
## Lag 0 1.00000000
## Lag 1e+05 -0.03154152
## Lag 2e+05 -0.04540585
## Lag 3e+05 0.00102928
## Lag 4e+05 -0.02230002
## Lag 5e+05 -0.01111823
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.012912349 0.0084845483 -0.0103706780
## Lag 2e+05 -0.001994479 -0.0038838674 0.0156029241
## Lag 3e+05 -0.001324666 0.0049230681 0.0155268284
## Lag 4e+05 -0.022066024 -0.0267043042 0.0215430485
## Lag 5e+05 0.010666914 -0.0008148622 0.0004980592
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.007867186 -0.008773321 -0.012665969
## Lag 2e+05 0.004882148 -0.008836749 -0.015706595
## Lag 3e+05 0.008026628 0.007753655 -0.012247478
## Lag 4e+05 -0.020299065 -0.013087254 0.003170685
## Lag 5e+05 -0.013328686 -0.019227111 0.008586260
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.002901487 0.013958607 0.002103240
## Lag 2e+05 -0.019354702 0.015219702 -0.008607253
## Lag 3e+05 -0.003436107 -0.010506831 0.003252033
## Lag 4e+05 0.015225248 -0.016147658 -0.005759139
## Lag 5e+05 -0.008337202 0.002882331 0.018208769
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.001606929
## Lag 2e+05 0.004638391
## Lag 3e+05 0.014221249
## Lag 4e+05 0.004492374
## Lag 5e+05 -0.002668298
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.013357999 -0.024688436 0.013450679
## Lag 2e+05 -0.013439290 -0.003946531 0.002529023
## Lag 3e+05 -0.011429223 -0.008271233 -0.015790217
## Lag 4e+05 0.019035307 0.010506640 0.010941237
## Lag 5e+05 0.001421248 0.004295797 -0.008716897
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.022234893 -0.009647018 -0.027540949
## Lag 2e+05 -0.014895510 -0.025062401 -0.014680512
## Lag 3e+05 -0.007155976 -0.019444813 -0.006754539
## Lag 4e+05 -0.008482358 0.013127562 -0.015949691
## Lag 5e+05 0.026320673 -0.003592270 -0.009814384
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.013309315 0.011555677 -0.019676039
## Lag 2e+05 0.010165583 -0.015543039 -0.013009029
## Lag 3e+05 -0.007021470 -0.010458431 0.022492434
## Lag 4e+05 -0.037763202 0.001802079 0.025786467
## Lag 5e+05 0.009848782 0.026872257 0.004224709
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.005168424
## Lag 2e+05 -0.027125651
## Lag 3e+05 -0.008583283
## Lag 4e+05 0.033031827
## Lag 5e+05 0.022661333
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.008253534 0.015898759 0.032063248
## Lag 2e+05 0.002474595 0.021039335 -0.023615082
## Lag 3e+05 0.023619901 0.024602429 -0.004488667
## Lag 4e+05 -0.026360181 -0.006133302 0.006938368
## Lag 5e+05 0.019182125 0.012223013 0.005978271
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000e+00
## Lag 1e+05 0.005115454 -0.030780080 -8.062468e-03
## Lag 2e+05 0.000484256 0.006346438 -3.433132e-05
## Lag 3e+05 0.012767446 0.017141250 2.749321e-02
## Lag 4e+05 -0.006862961 -0.008052323 -2.778476e-02
## Lag 5e+05 0.012875013 -0.006732726 2.243729e-03
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.018704893 0.011914458 0.0039600651
## Lag 2e+05 -0.017131962 0.015882479 0.0115316266
## Lag 3e+05 0.001827136 0.006646805 -0.0006106429
## Lag 4e+05 0.019590327 -0.002387818 -0.0185101675
## Lag 5e+05 0.012888176 0.008613483 0.0248701675
## absdiff.sqrt.age
## Lag 0 1.0000000000
## Lag 1e+05 0.0007974126
## Lag 2e+05 0.0071893861
## Lag 3e+05 -0.0040030835
## Lag 4e+05 -0.0064511000
## Lag 5e+05 0.0031036223
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0001802699 0.008685442 0.046315981
## Lag 2e+05 -0.0049541976 -0.008099083 -0.004700357
## Lag 3e+05 -0.0033122765 0.002586778 0.010180023
## Lag 4e+05 0.0032572790 0.001953264 0.004077353
## Lag 5e+05 -0.0100894642 0.001063313 -0.009567668
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.049855195 0.0149911755 -0.0005039103
## Lag 2e+05 0.016154316 0.0037906499 -0.0036587992
## Lag 3e+05 0.018223495 -0.0346875328 0.0143919830
## Lag 4e+05 -0.014941266 0.0001791233 0.0220529506
## Lag 5e+05 0.007956114 -0.0259601736 -0.0213489461
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.012889698 0.017631250 -0.011776658
## Lag 2e+05 0.019958925 0.015222414 -0.015334751
## Lag 3e+05 -0.007140378 0.003454250 -0.002745299
## Lag 4e+05 0.004748315 -0.003132181 0.005132262
## Lag 5e+05 -0.018065832 0.001888240 -0.012204515
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.021901412
## Lag 2e+05 -0.016096734
## Lag 3e+05 -0.002502099
## Lag 4e+05 -0.007300720
## Lag 5e+05 0.002563104
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.011742886 -0.011416213 -0.0004473439
## Lag 2e+05 -0.009586745 0.005073916 0.0044085935
## Lag 3e+05 -0.002078646 -0.003268665 -0.0053718574
## Lag 4e+05 0.012356225 0.019547148 0.0107660564
## Lag 5e+05 0.015798013 0.010098175 0.0279004412
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0300636792 -0.002017380 -0.0008925593
## Lag 2e+05 0.0150791552 -0.009122804 -0.0288135579
## Lag 3e+05 -0.0003032291 -0.009722598 -0.0149616597
## Lag 4e+05 0.0192007382 0.016900698 0.0035640685
## Lag 5e+05 -0.0227410332 -0.004199823 -0.0109229387
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.005219392 0.023250794 -0.004937892
## Lag 2e+05 0.011332192 0.012948200 -0.026657161
## Lag 3e+05 -0.013193865 0.004780372 0.005862342
## Lag 4e+05 -0.014399964 0.028150625 0.009929328
## Lag 5e+05 0.014346074 -0.025629618 0.017369591
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.012501484
## Lag 2e+05 0.002732422
## Lag 3e+05 -0.005926844
## Lag 4e+05 0.036510532
## Lag 5e+05 -0.012708863
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.3275 -2.1777 0.0214
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.5890 -0.8254 -1.1016
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8803 0.1926 -0.7592
## absdiff.sqrt.age
## -0.5819
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.18435506 0.02942797 0.98292434
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.11206200 0.40917087 0.27063277
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.37871390 0.84723488 0.44773132
## absdiff.sqrt.age
## 0.56062253
## Joint P-value (lower = worse): 0.2891199 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.364236 0.436802 -0.251930
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.897960 -0.112579 -0.002702
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.245986 1.376119 -0.784950
## absdiff.sqrt.age
## 0.028548
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7156819 0.6622548 0.8010949
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3692067 0.9103640 0.9978444
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8056932 0.1687848 0.4324831
## absdiff.sqrt.age
## 0.9772252
## Joint P-value (lower = worse): 0.8827967 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.6407 0.9586 -1.6891
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.2732 0.8059 -0.1146
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.2465 0.4348 -0.2693
## absdiff.sqrt.age
## 0.3946
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.52171888 0.33775463 0.09120271
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.78471004 0.42028388 0.90878777
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.21257724 0.66367593 0.78771062
## absdiff.sqrt.age
## 0.69314732
## Joint P-value (lower = worse): 0.1910543 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7890 1.2221 -1.7042
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.1695 0.2208 0.8489
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.1499 1.4493 1.9785
## absdiff.sqrt.age
## 0.1967
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.43012481 0.22165883 0.08834964
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.86537594 0.82528423 0.39594956
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.25017549 0.14725060 0.04787553
## absdiff.sqrt.age
## 0.84408351
## Joint P-value (lower = worse): 0.3636991 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 2.0294 1.0103 -1.3629
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.2965 0.1624 0.2034
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.1608 1.1216 1.5055
## absdiff.sqrt.age
## 1.2263
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.04241846 0.31237014 0.17290991
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.19481520 0.87100672 0.83883526
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.24572014 0.26204655 0.13220562
## absdiff.sqrt.age
## 0.22008473
## Joint P-value (lower = worse): 0.2377963 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3757 -0.8571 -1.3871
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.4681 -0.9130 -0.2103
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.9302 -0.7613 0.9851
## absdiff.sqrt.age
## 0.4068
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7071388 0.3913866 0.1654066
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.1420857 0.3612551 0.8334070
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.3522914 0.4464877 0.3245971
## absdiff.sqrt.age
## 0.6841911
## Joint P-value (lower = worse): 0.5337654 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.1597 0.3011 -0.9576
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.5312 -0.1916 -0.2759
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.0039 -0.1191 1.0372
## absdiff.sqrt.age
## 0.3885
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8731123 0.7633553 0.3382472
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5952904 0.8480210 0.7826535
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.3154118 0.9051800 0.2996273
## absdiff.sqrt.age
## 0.6976633
## Joint P-value (lower = worse): 0.8806737 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5718 -0.1604 -0.6994
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.2117 1.0104 0.1837
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1305 0.5924 0.8583
## absdiff.sqrt.age
## 0.1324
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5674725 0.8725316 0.4842915
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8323300 0.3123027 0.8542439
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8961811 0.5535766 0.3907472
## absdiff.sqrt.age
## 0.8946793
## Joint P-value (lower = worse): 0.9678352 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 7
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 2.56020 58.391 0.33712 0.35249
## nodefactor.deg.main.1 1.74833 60.531 0.34948 0.35604
## nodefactor.race..wa.B -0.46277 19.538 0.11280 0.11922
## nodefactor.race..wa.H 0.34290 29.713 0.17155 0.18463
## nodefactor.region.EW 0.07413 23.503 0.13570 0.13976
## nodefactor.region.OW 2.03730 47.684 0.27530 0.28500
## concurrent 2.35600 52.251 0.30167 0.31542
## nodematch.race..wa.B -0.12498 2.958 0.01708 0.01791
## nodematch.race..wa.H 0.22560 7.394 0.04269 0.04895
## nodematch.race..wa.O 2.47709 44.361 0.25612 0.26483
## absdiff.sqrt.age 3.87738 57.449 0.33168 0.33551
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -111.50 -37.500 2.5000 42.500 116.50
## nodefactor.deg.main.1 -117.00 -39.000 2.0000 43.000 120.00
## nodefactor.race..wa.B -38.52 -13.517 -0.5168 12.483 38.48
## nodefactor.race..wa.H -57.34 -20.340 0.6600 20.660 58.66
## nodefactor.region.EW -45.59 -15.588 -0.5885 16.412 46.41
## nodefactor.region.OW -90.25 -30.255 1.7450 33.745 96.75
## concurrent -100.00 -33.000 2.0000 38.000 105.00
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 6.52
## nodematch.race..wa.H -13.18 -5.181 -0.1815 4.819 15.82
## nodematch.race..wa.O -83.08 -28.081 1.9192 31.919 89.92
## absdiff.sqrt.age -106.95 -35.317 3.6019 42.581 117.23
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.81532396
## nodefactor.deg.main.1 0.81532396 1.00000000
## nodefactor.race..wa.B 0.40940550 0.31666461
## nodefactor.race..wa.H 0.53983925 0.48026138
## nodefactor.region.EW 0.46367902 0.37851973
## nodefactor.region.OW 0.73369985 0.56627424
## concurrent 0.95337514 0.77428823
## nodematch.race..wa.B 0.08308939 0.06171822
## nodematch.race..wa.H 0.16762233 0.16518989
## nodematch.race..wa.O 0.84248329 0.67197148
## absdiff.sqrt.age 0.84390304 0.68902688
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.40940550 0.53983925
## nodefactor.deg.main.1 0.31666461 0.48026138
## nodefactor.race..wa.B 1.00000000 0.18652647
## nodefactor.race..wa.H 0.18652647 1.00000000
## nodefactor.region.EW 0.14882036 0.35058572
## nodefactor.region.OW 0.27779864 0.37415021
## concurrent 0.39628243 0.52824275
## nodematch.race..wa.B 0.36149729 0.01141444
## nodematch.race..wa.H 0.01880424 0.56208942
## nodematch.race..wa.O 0.08861357 0.11072056
## absdiff.sqrt.age 0.34356660 0.45917886
## nodefactor.region.EW nodefactor.region.OW concurrent
## edges 0.46367902 0.73369985 0.95337514
## nodefactor.deg.main.1 0.37851973 0.56627424 0.77428823
## nodefactor.race..wa.B 0.14882036 0.27779864 0.39628243
## nodefactor.race..wa.H 0.35058572 0.37415021 0.52824275
## nodefactor.region.EW 1.00000000 0.21260844 0.43892589
## nodefactor.region.OW 0.21260844 1.00000000 0.69444399
## concurrent 0.43892589 0.69444399 1.00000000
## nodematch.race..wa.B 0.01808728 0.05140923 0.07919731
## nodematch.race..wa.H 0.13950353 0.11100574 0.16821251
## nodematch.race..wa.O 0.35349339 0.63647814 0.79437030
## absdiff.sqrt.age 0.39247342 0.62043941 0.80182799
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.08308939 0.167622327
## nodefactor.deg.main.1 0.06171822 0.165189888
## nodefactor.race..wa.B 0.36149729 0.018804236
## nodefactor.race..wa.H 0.01141444 0.562089421
## nodefactor.region.EW 0.01808728 0.139503533
## nodefactor.region.OW 0.05140923 0.111005737
## concurrent 0.07919731 0.168212515
## nodematch.race..wa.B 1.00000000 -0.012255626
## nodematch.race..wa.H -0.01225563 1.000000000
## nodematch.race..wa.O 0.01475949 0.006785681
## absdiff.sqrt.age 0.06875455 0.143950560
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.842483286 0.84390304
## nodefactor.deg.main.1 0.671971482 0.68902688
## nodefactor.race..wa.B 0.088613567 0.34356660
## nodefactor.race..wa.H 0.110720562 0.45917886
## nodefactor.region.EW 0.353493391 0.39247342
## nodefactor.region.OW 0.636478138 0.62043941
## concurrent 0.794370299 0.80182799
## nodematch.race..wa.B 0.014759487 0.06875455
## nodematch.race..wa.H 0.006785681 0.14395056
## nodematch.race..wa.O 1.000000000 0.70950037
## absdiff.sqrt.age 0.709500368 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.037286150 0.053973070 0.083178684
## Lag 2e+05 0.023799557 0.024959716 0.024622302
## Lag 3e+05 0.002384504 0.006625950 -0.019009190
## Lag 4e+05 0.001583185 -0.002520842 -0.006808975
## Lag 5e+05 -0.014736850 -0.004921189 -0.019600725
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.064708674 0.025235989 0.024533318
## Lag 2e+05 0.010123800 -0.023004175 0.019214796
## Lag 3e+05 0.013486204 -0.009496313 0.009793142
## Lag 4e+05 0.005749365 -0.017052115 0.030921503
## Lag 5e+05 0.003898799 0.005610631 -0.019661396
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 0.037093337 4.081248e-02 0.129533238
## Lag 2e+05 0.004351162 3.644228e-03 0.045049292
## Lag 3e+05 0.008055477 1.653099e-02 0.031994256
## Lag 4e+05 0.004681985 -1.177319e-03 0.015435097
## Lag 5e+05 -0.014824707 -7.333728e-06 0.008942917
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.01814904 0.002137389
## Lag 2e+05 0.01712617 0.011427675
## Lag 3e+05 0.01077021 -0.018317130
## Lag 4e+05 0.01235665 0.011996843
## Lag 5e+05 -0.02694711 0.004830735
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.029810042 0.0247548076 0.062691605
## Lag 2e+05 0.001266106 0.0033180904 0.015261145
## Lag 3e+05 0.036104133 0.0099935405 0.026363561
## Lag 4e+05 0.002473999 -0.0054341546 -0.005960574
## Lag 5e+05 0.004890703 0.0002319202 -0.005521103
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.043075754 0.0426253015 0.045429100
## Lag 2e+05 0.024111558 -0.0028820058 -0.008053735
## Lag 3e+05 -0.004511711 0.0154625712 0.036576616
## Lag 4e+05 0.001945730 -0.0112721333 0.006491514
## Lag 5e+05 -0.001830603 0.0001993954 0.001757440
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.033181117 0.06751044 0.11669525
## Lag 2e+05 -0.002665167 0.00537341 0.01832427
## Lag 3e+05 0.031986078 0.01777450 0.01481518
## Lag 4e+05 0.008663433 -0.01545655 0.01465160
## Lag 5e+05 0.000642860 -0.01476121 -0.01128883
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.036241834 0.017711444
## Lag 2e+05 0.005172482 0.005439962
## Lag 3e+05 0.037093887 0.015267689
## Lag 4e+05 0.015096004 0.004385634
## Lag 5e+05 0.026496799 0.004792809
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.045994832 0.029266468 0.070185868
## Lag 2e+05 0.008519233 -0.005950597 0.022486965
## Lag 3e+05 -0.006237169 -0.007808244 -0.017282285
## Lag 4e+05 0.029173872 0.030815002 -0.010547668
## Lag 5e+05 0.013070976 -0.003110699 -0.005951494
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.063722769 0.024333436 0.024118209
## Lag 2e+05 0.035904931 0.009959550 -0.002207011
## Lag 3e+05 0.021359563 0.023246275 -0.005401745
## Lag 4e+05 0.006659831 -0.006364673 0.017542774
## Lag 5e+05 -0.005102857 -0.002660498 0.002294958
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.051655639 0.026600675 0.121305968
## Lag 2e+05 0.014867896 0.007572277 0.034444056
## Lag 3e+05 0.005346994 0.001751777 -0.005108472
## Lag 4e+05 0.030733697 -0.005029932 0.007177923
## Lag 5e+05 0.008712063 -0.005668931 -0.004320882
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.028080372 0.01752561
## Lag 2e+05 0.013215180 -0.01276761
## Lag 3e+05 -0.008126697 -0.01992698
## Lag 4e+05 0.001219329 0.01060865
## Lag 5e+05 0.009198453 0.00631485
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.046979438 0.059636063 0.035060817
## Lag 2e+05 -0.009646163 -0.013474128 0.001079246
## Lag 3e+05 0.003323737 -0.011176088 0.019682855
## Lag 4e+05 -0.023260995 -0.028660818 0.028032824
## Lag 5e+05 0.007204432 -0.007629623 0.004375414
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000e+00
## Lag 1e+05 0.0591349911 0.018943004 1.950200e-02
## Lag 2e+05 -0.0004480031 -0.002725787 -2.019611e-02
## Lag 3e+05 -0.0042947580 0.027014547 9.317803e-05
## Lag 4e+05 -0.0150846125 -0.007041113 -9.807235e-03
## Lag 5e+05 0.0048232534 -0.006120898 8.660884e-03
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0444343249 0.053761175 0.110710492
## Lag 2e+05 -0.0062045059 -0.013456352 0.032585937
## Lag 3e+05 0.0004976362 -0.011842201 -0.006273306
## Lag 4e+05 -0.0249906751 -0.003846662 0.007270896
## Lag 5e+05 0.0050188098 -0.010614518 -0.012300748
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.039635059 -0.000505848
## Lag 2e+05 0.009374158 -0.012724647
## Lag 3e+05 0.007849791 -0.014672800
## Lag 4e+05 -0.005207145 -0.014733071
## Lag 5e+05 -0.006241605 0.012576622
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.069829312 0.041938982 0.04184465
## Lag 2e+05 -0.005701886 -0.017335699 0.02578281
## Lag 3e+05 0.015282344 -0.006897152 0.01425194
## Lag 4e+05 0.009266438 0.029771024 0.01811135
## Lag 5e+05 -0.013286560 -0.009683505 0.01965237
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.077564787 0.066482959 0.03675020
## Lag 2e+05 0.016836957 0.017137486 -0.01972807
## Lag 3e+05 0.012654103 -0.023918516 0.01438247
## Lag 4e+05 0.009452725 -0.008100835 0.03091076
## Lag 5e+05 -0.004527649 0.004439086 -0.00152464
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.066462975 0.044381718 0.1286658329
## Lag 2e+05 -0.013796907 -0.015332947 0.0221688051
## Lag 3e+05 0.006346225 0.022774605 0.0025354422
## Lag 4e+05 -0.001672189 0.009221063 0.0090288603
## Lag 5e+05 -0.010621236 -0.002448240 -0.0005961641
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.060382109 0.046926106
## Lag 2e+05 -0.016637481 -0.010075757
## Lag 3e+05 0.017354635 0.014745149
## Lag 4e+05 0.007179554 0.007226805
## Lag 5e+05 -0.016321773 -0.008523600
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.038116307 0.0256461226 0.050785980
## Lag 2e+05 -0.010911659 -0.0095280823 -0.012031487
## Lag 3e+05 -0.017010026 0.0001268855 -0.018595993
## Lag 4e+05 0.010620751 0.0122613897 0.006962936
## Lag 5e+05 -0.001511979 0.0024015302 -0.007845244
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.065610296 0.039242456 0.053204137
## Lag 2e+05 0.006331961 0.001917704 0.008586125
## Lag 3e+05 0.005067413 -0.015394618 -0.008686808
## Lag 4e+05 -0.008404839 0.010967022 0.009468107
## Lag 5e+05 0.012183647 -0.031621516 -0.020683961
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0487649215 0.070290825 0.1385022836
## Lag 2e+05 -0.0153537173 -0.001692911 0.0145275078
## Lag 3e+05 -0.0026966517 -0.015234988 0.0105130579
## Lag 4e+05 0.0035381043 -0.011034684 -0.0080989193
## Lag 5e+05 -0.0007243528 0.011069148 -0.0002954398
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.036937039 -0.003971490
## Lag 2e+05 0.015793976 0.016749882
## Lag 3e+05 -0.007192037 -0.006522373
## Lag 4e+05 -0.009100066 0.011594334
## Lag 5e+05 -0.021438329 0.002404723
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.078280610 0.070802879 0.0328512933
## Lag 2e+05 -0.025087534 -0.019529753 0.0009767469
## Lag 3e+05 0.015938364 0.007005649 -0.0159548415
## Lag 4e+05 0.004576905 -0.012997840 -0.0101964851
## Lag 5e+05 0.015553809 -0.007698634 -0.0117533496
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.097424481 0.057011262 0.0534904598
## Lag 2e+05 0.008153905 0.022433953 -0.0287604036
## Lag 3e+05 0.025298946 0.017477612 0.0009888269
## Lag 4e+05 0.003954406 0.003167544 0.0081740436
## Lag 5e+05 -0.013938974 -0.016393943 0.0318674514
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.074061586 0.05705956 0.126969352
## Lag 2e+05 -0.023538252 -0.02992377 0.026454998
## Lag 3e+05 0.020286598 -0.00794342 -0.009863259
## Lag 4e+05 -0.001837354 -0.01674762 -0.016449983
## Lag 5e+05 0.001728648 -0.02388094 -0.005241626
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.049047685 0.040284976
## Lag 2e+05 -0.028003094 -0.009788137
## Lag 3e+05 -0.006704590 0.018804934
## Lag 4e+05 0.001765729 0.007836237
## Lag 5e+05 0.021771202 0.006542356
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.014492698 0.025203998 0.038957303
## Lag 2e+05 -0.001342423 0.009104508 -0.004216668
## Lag 3e+05 -0.010031467 -0.032178336 0.017529350
## Lag 4e+05 -0.017283374 -0.015368051 0.024523450
## Lag 5e+05 0.013102579 0.018108061 0.009978828
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.06438740 -0.004589530 0.039568590
## Lag 2e+05 0.02702030 0.008517797 0.004413620
## Lag 3e+05 -0.01772278 0.010960372 -0.015493205
## Lag 4e+05 -0.03611078 -0.015312436 -0.004144681
## Lag 5e+05 0.04106608 0.006097113 0.023200539
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.017398731 0.049210221 0.128279635
## Lag 2e+05 0.004721312 0.003032944 0.043719388
## Lag 3e+05 -0.009112096 -0.001167766 0.038539044
## Lag 4e+05 -0.016400998 -0.018298393 -0.008672095
## Lag 5e+05 0.004889841 -0.013433095 0.019190510
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0005281401 -0.007672802
## Lag 2e+05 -0.0076028553 0.012468414
## Lag 3e+05 0.0154958465 -0.005035327
## Lag 4e+05 -0.0017618691 -0.006923323
## Lag 5e+05 -0.0203466309 0.018639543
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.95086 -0.85572 -2.35064
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.22592 0.05783 -0.28661
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.56666 0.45455 1.15390
## nodematch.race..wa.O absdiff.sqrt.age
## -0.44587 -0.04987
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.34167527 0.39215465 0.01874106
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.82126216 0.95388023 0.77440863
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.57094184 0.64943362 0.24854129
## nodematch.race..wa.O absdiff.sqrt.age
## 0.65569447 0.96022224
## Joint P-value (lower = worse): 0.1724949 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.25537 0.23737 0.56534
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.07279 -0.52720 0.31301
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.53958 -0.32990 -1.75500
## nodematch.race..wa.O absdiff.sqrt.age
## -0.02794 -0.02963
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7984390 0.8123734 0.5718442
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9419732 0.5980539 0.7542742
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.5894876 0.7414735 0.0792598
## nodematch.race..wa.O absdiff.sqrt.age
## 0.9777087 0.9763628
## Joint P-value (lower = worse): 0.8106185 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.8307 -1.9603 -0.4892
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.7056 -0.4852 -2.6789
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -1.4575 -0.5368 1.0163
## nodematch.race..wa.O absdiff.sqrt.age
## -1.9648 -1.9387
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.06714026 0.04996150 0.62467897
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.48042409 0.62753830 0.00738554
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.14498322 0.59139726 0.30949636
## nodematch.race..wa.O absdiff.sqrt.age
## 0.04943181 0.05253321
## Joint P-value (lower = worse): 0.1388281 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.54246 0.01329 0.75142
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.19336 0.51226 0.54583
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.73670 -2.05268 -0.72858
## nodematch.race..wa.O absdiff.sqrt.age
## 0.32310 0.44429
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.58750075 0.98939541 0.45240186
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.84667823 0.60846775 0.58518139
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.46130479 0.04010362 0.46625914
## nodematch.race..wa.O absdiff.sqrt.age
## 0.74661640 0.65683298
## Joint P-value (lower = worse): 0.5998682 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.7703 -0.7951 -0.1447
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.4544 1.0915 -0.6894
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.0161 -1.7074 1.3674
## nodematch.race..wa.O absdiff.sqrt.age
## -1.0228 0.1336
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.44114243 0.42658118 0.88491260
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.64953547 0.27506236 0.49057987
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.98715684 0.08774392 0.17150397
## nodematch.race..wa.O absdiff.sqrt.age
## 0.30639226 0.89370692
## Joint P-value (lower = worse): 0.1115582 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.7432 -0.4806 -1.8145
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3513 0.4665 0.3618
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.4552 -1.6340 0.3979
## nodematch.race..wa.O absdiff.sqrt.age
## -0.4570 -0.5523
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.45735013 0.63080676 0.06959447
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.72533686 0.64084048 0.71747586
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.64897426 0.10226746 0.69068901
## nodematch.race..wa.O absdiff.sqrt.age
## 0.64768824 0.58074698
## Joint P-value (lower = worse): 0.5455631 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.94331 1.51127 -0.07321
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.59393 -0.30278 0.77654
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.92448 -0.17841 -0.26814
## nodematch.race..wa.O absdiff.sqrt.age
## 1.48486 1.31954
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3455200 0.1307198 0.9416429
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5525599 0.7620547 0.4374317
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.3552371 0.8584000 0.7885940
## nodematch.race..wa.O absdiff.sqrt.age
## 0.1375820 0.1869872
## Joint P-value (lower = worse): 0.8577423 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3334 -0.3222 -1.0021
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.3851 -1.0456 -0.1218
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.5332 0.3746 -0.2185
## nodematch.race..wa.O absdiff.sqrt.age
## 0.1865 0.2666
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7388402 0.7473110 0.3163088
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.7001695 0.2957361 0.9030921
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.5939285 0.7079896 0.8270315
## nodematch.race..wa.O absdiff.sqrt.age
## 0.8520545 0.7897682
## Joint P-value (lower = worse): 0.9469402 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 8
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.66680 58.448 0.33745 0.36719
## nodefactor.deg.main.1 -0.28323 60.598 0.34987 0.39071
## nodefactor.race..wa.B 0.45133 19.551 0.11288 0.12474
## nodefactor.race..wa.H -0.40497 29.565 0.17069 0.20151
## nodefactor.region.EW -0.35553 29.095 0.16798 0.22619
## nodefactor.region.OW 1.52177 58.429 0.33734 0.38532
## concurrent 0.65537 52.479 0.30299 0.33920
## nodematch.race..wa.B 0.01225 2.959 0.01709 0.01936
## nodematch.race..wa.H -0.11196 7.367 0.04254 0.05847
## nodematch.race..wa.O 0.62632 44.390 0.25629 0.28304
## nodematch.region 0.79673 50.110 0.28931 0.32255
## absdiff.sqrt.age 0.23514 57.200 0.33024 0.34861
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -113.50 -38.500 0.50000 40.500 115.50
## nodefactor.deg.main.1 -117.00 -41.000 0.00000 40.000 120.00
## nodefactor.race..wa.B -37.52 -12.517 0.48320 13.483 39.48
## nodefactor.race..wa.H -59.34 -20.340 -0.34000 19.660 57.66
## nodefactor.region.EW -56.59 -20.588 -0.58850 19.412 58.41
## nodefactor.region.OW -112.25 -38.255 0.74500 40.745 115.75
## concurrent -101.00 -35.000 0.00000 36.000 104.00
## nodematch.race..wa.B -5.48 -2.480 -0.47985 1.520 6.52
## nodematch.race..wa.H -14.18 -5.181 -0.18150 4.819 14.82
## nodematch.race..wa.O -86.08 -29.081 -0.08078 29.919 88.92
## nodematch.region -97.00 -33.000 1.00000 34.000 99.00
## absdiff.sqrt.age -111.00 -38.218 -0.42208 38.203 114.40
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.81456784
## nodefactor.deg.main.1 0.81456784 1.00000000
## nodefactor.race..wa.B 0.40935667 0.30881917
## nodefactor.race..wa.H 0.53767302 0.47318457
## nodefactor.region.EW 0.38372755 0.31293465
## nodefactor.region.OW 0.61476344 0.44840452
## concurrent 0.95340527 0.77306159
## nodematch.race..wa.B 0.07765574 0.05024095
## nodematch.race..wa.H 0.16467644 0.15594565
## nodematch.race..wa.O 0.84442801 0.67789983
## nodematch.region 0.93038530 0.76251573
## absdiff.sqrt.age 0.84574946 0.68769371
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.40935667 0.53767302
## nodefactor.deg.main.1 0.30881917 0.47318457
## nodefactor.race..wa.B 1.00000000 0.18166470
## nodefactor.race..wa.H 0.18166470 1.00000000
## nodefactor.region.EW 0.09764293 0.34586107
## nodefactor.region.OW 0.21743022 0.31827692
## concurrent 0.39614937 0.52278941
## nodematch.race..wa.B 0.36437062 0.01266853
## nodematch.race..wa.H 0.02020527 0.56056088
## nodematch.race..wa.O 0.09229356 0.11163319
## nodematch.region 0.39016704 0.48281919
## absdiff.sqrt.age 0.34885504 0.45685588
## nodefactor.region.EW nodefactor.region.OW concurrent
## edges 0.383727554 0.61476344 0.95340527
## nodefactor.deg.main.1 0.312934654 0.44840452 0.77306159
## nodefactor.race..wa.B 0.097642931 0.21743022 0.39614937
## nodefactor.race..wa.H 0.345861071 0.31827692 0.52278941
## nodefactor.region.EW 1.000000000 0.11159336 0.36057873
## nodefactor.region.OW 0.111593364 1.00000000 0.57699309
## concurrent 0.360578729 0.57699309 1.00000000
## nodematch.race..wa.B 0.006754638 0.03740027 0.07665564
## nodematch.race..wa.H 0.164992910 0.10041914 0.16434475
## nodematch.race..wa.O 0.273051536 0.53839931 0.79818692
## nodematch.region 0.253396240 0.53932461 0.88909544
## absdiff.sqrt.age 0.325989762 0.52057949 0.80432294
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.077655738 0.164676442
## nodefactor.deg.main.1 0.050240951 0.155945653
## nodefactor.race..wa.B 0.364370624 0.020205271
## nodefactor.race..wa.H 0.012668525 0.560560879
## nodefactor.region.EW 0.006754638 0.164992910
## nodefactor.region.OW 0.037400274 0.100419144
## concurrent 0.076655640 0.164344753
## nodematch.race..wa.B 1.000000000 0.002924091
## nodematch.race..wa.H 0.002924091 1.000000000
## nodematch.race..wa.O 0.006223547 0.003378771
## nodematch.region 0.073980255 0.143059365
## absdiff.sqrt.age 0.064027496 0.137716482
## nodematch.race..wa.O nodematch.region
## edges 0.844428009 0.93038530
## nodefactor.deg.main.1 0.677899825 0.76251573
## nodefactor.race..wa.B 0.092293565 0.39016704
## nodefactor.race..wa.H 0.111633191 0.48281919
## nodefactor.region.EW 0.273051536 0.25339624
## nodefactor.region.OW 0.538399314 0.53932461
## concurrent 0.798186920 0.88909544
## nodematch.race..wa.B 0.006223547 0.07398025
## nodematch.race..wa.H 0.003378771 0.14305937
## nodematch.race..wa.O 1.000000000 0.79153679
## nodematch.region 0.791536788 1.00000000
## absdiff.sqrt.age 0.711535111 0.78584345
## absdiff.sqrt.age
## edges 0.8457495
## nodefactor.deg.main.1 0.6876937
## nodefactor.race..wa.B 0.3488550
## nodefactor.race..wa.H 0.4568559
## nodefactor.region.EW 0.3259898
## nodefactor.region.OW 0.5205795
## concurrent 0.8043229
## nodematch.race..wa.B 0.0640275
## nodematch.race..wa.H 0.1377165
## nodematch.race..wa.O 0.7115351
## nodematch.region 0.7858435
## absdiff.sqrt.age 1.0000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.074293832 0.103184510 0.099417520
## Lag 2e+05 0.003235864 0.007750902 0.032188678
## Lag 3e+05 0.019147412 0.009635380 0.006955118
## Lag 4e+05 0.017612327 0.027764309 0.011395681
## Lag 5e+05 -0.006474480 -0.007160272 -0.023443624
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.14509585 0.207746950 0.136487350
## Lag 2e+05 0.03518996 0.084236194 0.030927399
## Lag 3e+05 0.03735473 0.042162927 0.016833797
## Lag 4e+05 -0.01440172 -0.003179383 0.006021293
## Lag 5e+05 -0.01985848 -0.017586317 0.007254426
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.085703515 0.101190378 0.232560491
## Lag 2e+05 0.011717952 0.014873165 0.085772228
## Lag 3e+05 0.006315926 -0.017897670 0.050480378
## Lag 4e+05 0.007808545 0.001998121 -0.004240963
## Lag 5e+05 -0.003418756 -0.012928698 0.012982839
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.073623014 0.108060165 0.013346907
## Lag 2e+05 0.012916930 0.027019565 0.003174847
## Lag 3e+05 -0.009078349 0.016055797 0.004733757
## Lag 4e+05 0.023761772 -0.005430556 0.006818297
## Lag 5e+05 0.002785007 0.002187026 -0.017992580
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.115223816 0.125460243 0.1193641005
## Lag 2e+05 0.009324493 0.025916692 0.0154064194
## Lag 3e+05 0.012966545 0.014875380 -0.0009208817
## Lag 4e+05 0.024082652 0.005666413 0.0077636945
## Lag 5e+05 0.032722502 -0.003934597 0.0180051689
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.15064462 0.22376202 0.152869891
## Lag 2e+05 0.04103333 0.11661253 0.017991300
## Lag 3e+05 0.02424551 0.04612830 -0.005044468
## Lag 4e+05 0.01232480 0.03524642 0.012214286
## Lag 5e+05 0.01802221 0.03528585 0.011701918
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.125260272 0.08480411 0.24172748
## Lag 2e+05 0.009617682 0.03928150 0.09046472
## Lag 3e+05 0.008979788 -0.01032875 0.04097252
## Lag 4e+05 0.024174416 -0.01563746 0.04167515
## Lag 5e+05 0.038257803 -0.02714986 0.04166747
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.102373798 0.130918465 0.070129431
## Lag 2e+05 0.005066625 -0.002073079 0.002905888
## Lag 3e+05 0.006226339 0.022411360 -0.011380755
## Lag 4e+05 0.038630460 0.016027926 0.010600987
## Lag 5e+05 0.054250347 0.030869334 0.011872936
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.087882032 0.091039267 0.121189900
## Lag 2e+05 0.020945280 0.041463336 0.021004095
## Lag 3e+05 0.004190995 0.010480954 -0.028531850
## Lag 4e+05 0.003060805 -0.006696759 -0.007880029
## Lag 5e+05 0.021879214 0.021980128 -0.005641812
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.131896432 0.22849262 0.104251287
## Lag 2e+05 0.045359576 0.10513004 0.015184035
## Lag 3e+05 -0.003647636 0.08003565 -0.035096920
## Lag 4e+05 0.010741760 0.03931201 -0.003852028
## Lag 5e+05 0.037280635 0.02563615 -0.012858144
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.102314640 0.077471881 0.28060387
## Lag 2e+05 0.034863788 0.033003690 0.09379429
## Lag 3e+05 0.010830524 0.003756270 0.03919141
## Lag 4e+05 -0.007357862 0.006302403 0.04841501
## Lag 5e+05 0.020597082 0.005428096 0.05800565
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.069197071 0.120046628 0.0428075022
## Lag 2e+05 0.017875164 0.015370300 0.0238138148
## Lag 3e+05 -0.004151606 -0.004578317 -0.0100426214
## Lag 4e+05 -0.011063107 0.016452838 -0.0062286812
## Lag 5e+05 0.003506228 0.015527336 -0.0002153367
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.09965993 0.085906354 0.095825777
## Lag 2e+05 0.02288362 0.013038705 0.027394229
## Lag 3e+05 0.01500735 0.037029784 -0.002300478
## Lag 4e+05 -0.01560653 -0.022260062 0.008738308
## Lag 5e+05 0.01242791 -0.001170029 0.009693686
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.00000000 1.0000000000
## Lag 1e+05 0.17597384 0.23764473 0.1184861441
## Lag 2e+05 0.05671462 0.12191994 0.0214990675
## Lag 3e+05 0.02751840 0.04664070 0.0135271955
## Lag 4e+05 0.02177744 0.04419933 -0.0006331608
## Lag 5e+05 0.01959448 0.03671348 0.0257683669
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.112858636 0.094418845 0.26739887
## Lag 2e+05 0.021544028 0.056460475 0.08951689
## Lag 3e+05 0.013182985 0.006552299 0.05954811
## Lag 4e+05 -0.012851059 0.001760424 0.05222515
## Lag 5e+05 0.002607713 -0.033753878 0.01679016
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000 1.000000e+00
## Lag 1e+05 0.07490300 0.126989905 6.148455e-02
## Lag 2e+05 0.01575161 0.026939046 2.384938e-02
## Lag 3e+05 0.01368416 0.026676327 6.261880e-03
## Lag 4e+05 -0.01814433 -0.007087429 -6.825193e-05
## Lag 5e+05 0.01411661 0.009143597 4.661058e-03
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.095829257 0.099756248 0.08681252
## Lag 2e+05 0.013053650 0.009667583 -0.02029464
## Lag 3e+05 -0.012695800 -0.001958401 0.01147690
## Lag 4e+05 -0.015301872 -0.008372395 -0.01105644
## Lag 5e+05 0.005846601 0.007889433 -0.01003685
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.149022359 0.21477377 0.12964445
## Lag 2e+05 0.058485212 0.10335735 0.03305176
## Lag 3e+05 0.023253319 0.05815442 -0.02474144
## Lag 4e+05 -0.005692117 0.01785135 -0.03134798
## Lag 5e+05 0.023873202 0.01607740 -0.01074687
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.108027150 0.120269981 0.22619595
## Lag 2e+05 0.016543501 0.014070368 0.07723189
## Lag 3e+05 0.003252081 -0.022820270 0.03353427
## Lag 4e+05 -0.001535082 0.006914825 0.02678001
## Lag 5e+05 0.008407682 -0.025428426 0.01978278
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.100896695 0.13316347 0.062489591
## Lag 2e+05 0.013927347 0.01350103 0.004510762
## Lag 3e+05 -0.004561992 -0.02151337 -0.006091791
## Lag 4e+05 -0.003427188 -0.02026785 -0.005896213
## Lag 5e+05 -0.006602846 -0.00211587 0.015244832
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.110822089 0.104475352 0.116854618
## Lag 2e+05 0.032670686 0.037658351 0.041640538
## Lag 3e+05 0.012880414 0.017445646 0.009049369
## Lag 4e+05 0.006163556 -0.012091604 -0.022953106
## Lag 5e+05 0.018532449 0.002957128 0.016554608
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.149949333 0.24470239 0.144931494
## Lag 2e+05 0.043272434 0.11025784 0.024020775
## Lag 3e+05 0.016379884 0.08454198 0.006030983
## Lag 4e+05 0.008830322 0.04888941 0.002409433
## Lag 5e+05 0.011196342 -0.01007933 0.017713875
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.118290139 0.130864654 0.24216763
## Lag 2e+05 0.028463444 0.022262901 0.08805779
## Lag 3e+05 0.016152060 -0.005293691 0.05253961
## Lag 4e+05 0.009733243 -0.005783728 0.03662347
## Lag 5e+05 0.009080203 -0.006199656 0.02953350
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.090577207 0.12445116 0.0539571400
## Lag 2e+05 0.031937051 0.03135497 -0.0158199301
## Lag 3e+05 0.006908005 0.02599254 0.0119028066
## Lag 4e+05 0.028890178 0.00485657 0.0006412076
## Lag 5e+05 0.006978902 0.02268425 0.0102243027
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.119655203 0.11629333 0.096200024
## Lag 2e+05 0.012186792 0.02028062 -0.005461918
## Lag 3e+05 -0.007695295 -0.01257345 0.008225973
## Lag 4e+05 -0.021553244 -0.02948419 -0.014846870
## Lag 5e+05 -0.018705821 -0.02577632 -0.006179736
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.15048807 0.248343014 0.16286658
## Lag 2e+05 0.03528786 0.076380143 0.05595228
## Lag 3e+05 -0.02799785 0.042653199 0.03046889
## Lag 4e+05 -0.03213655 0.016475017 0.02174683
## Lag 5e+05 -0.02936321 0.009942162 0.01197120
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.119063415 0.078827405 0.223165480
## Lag 2e+05 0.023225961 0.035913827 0.084158034
## Lag 3e+05 -0.002429621 0.008113151 0.015522006
## Lag 4e+05 -0.019807585 -0.014222378 0.006265332
## Lag 5e+05 -0.020237895 0.010747700 0.001661586
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.081664055 0.13986094 0.0713530831
## Lag 2e+05 -0.001422855 0.01114823 -0.0001825523
## Lag 3e+05 -0.004998221 -0.02907384 0.0034548259
## Lag 4e+05 -0.004114214 -0.03444100 -0.0214795508
## Lag 5e+05 0.007288660 -0.02503555 -0.0160214387
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.093343037 0.078285568 0.078901600
## Lag 2e+05 0.040636377 0.027985531 0.016820382
## Lag 3e+05 0.001246276 0.023158699 0.005714428
## Lag 4e+05 -0.005815376 -0.008688396 -0.007131129
## Lag 5e+05 -0.007561410 -0.003487521 -0.010611769
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.132503848 0.231695399 0.134358726
## Lag 2e+05 0.040672340 0.097945003 0.013834241
## Lag 3e+05 -0.013528752 0.019039257 0.002100438
## Lag 4e+05 -0.004497829 0.004624715 0.000404643
## Lag 5e+05 0.010497644 0.013923770 0.004361277
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.102417511 0.078739115 0.22207289
## Lag 2e+05 0.028443227 0.044502596 0.09783127
## Lag 3e+05 0.001307577 0.001499173 0.03131178
## Lag 4e+05 -0.007274084 0.032452400 0.01774579
## Lag 5e+05 -0.010498868 0.033350757 0.01915487
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.089375846 0.12369334 0.064247760
## Lag 2e+05 0.045304306 0.05186764 0.014606909
## Lag 3e+05 -0.013031036 0.01682722 0.017017466
## Lag 4e+05 -0.006798134 -0.01323113 0.015966310
## Lag 5e+05 -0.021934717 -0.01782833 0.007989782
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.4609 -1.0715 0.1384
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.1733 -0.2985 -0.9342
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.2804 1.9406 -1.4466
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.5649 -0.8217 -0.6377
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.64487253 0.28395647 0.88993828
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.86245326 0.76530043 0.35019126
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.77918346 0.05230778 0.14801322
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.57212503 0.41125800 0.52369479
## Joint P-value (lower = worse): 0.3054153 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.7290 1.3571 -0.1744
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.1600 0.4552 0.8827
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 1.8108 0.9515 -0.3113
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 1.2210 1.5909 1.2676
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.08380489 0.17475429 0.86155823
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.24604230 0.64894109 0.37741861
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.07017914 0.34132868 0.75555829
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.22209196 0.11162970 0.20494271
## Joint P-value (lower = worse): 0.6623661 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6686 0.6608 1.4712
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5310 -0.1524 0.1810
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.9611 0.7799 -0.1682
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.2012 0.5015 -0.3641
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5037695 0.5087711 0.1412246
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5954229 0.8789045 0.8563528
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.3365032 0.4354553 0.8664026
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.8405243 0.6160452 0.7157900
## Joint P-value (lower = worse): 0.8041529 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.9484 -1.5226 0.9036
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.1150 -0.9920 -0.7184
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -1.3198 0.8713 -0.6261
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.7878 -1.0664 -1.3658
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3429310 0.1278636 0.3661999
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.2648709 0.3212203 0.4725249
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.1868865 0.3835701 0.5312710
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.4308254 0.2862503 0.1720174
## Joint P-value (lower = worse): 0.637911 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.2413 -0.5338 -1.1720
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -2.0669 -1.1867 0.7389
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.6866 -0.4951 -1.7282
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.8545 -0.6194 0.1275
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.80935013 0.59350570 0.24117757
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.03874792 0.23535837 0.45999548
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.49235589 0.62049989 0.08395362
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.39280043 0.53566661 0.89855936
## Joint P-value (lower = worse): 0.380919 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.78487 0.50761 0.40398
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.81492 0.07568 0.96900
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.85771 0.25023 0.62921
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.31261 0.50497 1.23779
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.4325296 0.6117286 0.6862299
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.4151166 0.9396727 0.3325436
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.3910545 0.8024123 0.5292140
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.7545755 0.6135768 0.2157949
## Joint P-value (lower = worse): 0.9785587 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8311 0.6403 0.5042
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8659 0.3932 1.6586
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.7850 -2.2687 1.2192
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.1300 0.5541 0.9714
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.40594493 0.52199430 0.61409291
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.38654681 0.69414037 0.09720191
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.43242436 0.02328945 0.22278489
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.89658071 0.57954032 0.33133755
## Joint P-value (lower = worse): 0.2502095 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.27638 -0.38847 0.40707
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.28886 0.65683 -1.28733
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.06285 -0.46211 -0.23880
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.79642 -0.53646 -1.28862
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7822535 0.6976688 0.6839559
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.7726886 0.5112927 0.1979791
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.9498845 0.6440026 0.8112637
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.4257883 0.5916400 0.1975302
## Joint P-value (lower = worse): 0.17804 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
Model 1
summary(est.p.buildup.bal[[1]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55ba04e08fb0>
##
## Iterations: 80 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.92022 0.02474 0 <1e-04 ***
## deg3+ -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 2
summary(est.p.buildup.bal[[2]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55ba28d65d08>
##
## Iterations: 86 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.06381 0.03026 0 < 1e-04 ***
## nodefactor.race..wa.B 0.24745 0.06626 0 0.000188 ***
## nodefactor.race..wa.H 0.45289 0.04864 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 3
summary(est.p.buildup.bal[[3]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55ba46ecf200>
##
## Iterations: 95 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.5470 0.1530 0 < 1e-04 ***
## nodefactor.race..wa.B 0.6620 0.1380 0 < 1e-04 ***
## nodefactor.race..wa.H 0.8720 0.1462 0 < 1e-04 ***
## nodematch.race..wa.B -0.5210 0.3745 0 0.16413
## nodematch.race..wa.H -0.2335 0.2066 0 0.25853
## nodematch.race..wa.O 0.5010 0.1550 0 0.00122 **
## deg3+ -Inf 0.0000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.0000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.0000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 4
summary(est.p.buildup.bal[[4]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55ba6519a210>
##
## Iterations: 99 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.42309 0.15666 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.14241 0.03402 0 < 1e-04 ***
## nodefactor.race..wa.B 0.64936 0.13959 0 < 1e-04 ***
## nodefactor.race..wa.H 0.88531 0.14768 0 < 1e-04 ***
## nodematch.race..wa.B -0.52073 0.37622 0 0.16632
## nodematch.race..wa.H -0.23081 0.20857 0 0.26845
## nodematch.race..wa.O 0.49933 0.15653 0 0.00142 **
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 5
summary(est.p.buildup.bal[[5]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55ba8352dc20>
##
## Iterations: 83 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.22601 0.15921 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.16025 0.03412 0 < 1e-04 ***
## nodefactor.race..wa.B 0.62238 0.13904 0 < 1e-04 ***
## nodefactor.race..wa.H 0.90832 0.14718 0 < 1e-04 ***
## nodefactor.region.EW -0.25682 0.05965 0 < 1e-04 ***
## nodefactor.region.OW -0.21613 0.03732 0 < 1e-04 ***
## nodematch.race..wa.B -0.52022 0.37640 0 0.16694
## nodematch.race..wa.H -0.23307 0.20954 0 0.26602
## nodematch.race..wa.O 0.50157 0.15610 0 0.00131 **
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 6
summary(est.p.buildup.bal[[6]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55baa19f3c40>
##
## Iterations: 87 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.67402 0.16039 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.15920 0.03398 0 < 1e-04 ***
## nodefactor.race..wa.B 0.62469 0.13884 0 < 1e-04 ***
## nodefactor.race..wa.H 0.90777 0.14674 0 < 1e-04 ***
## nodefactor.region.EW -0.25784 0.05961 0 < 1e-04 ***
## nodefactor.region.OW -0.21543 0.03763 0 < 1e-04 ***
## nodematch.race..wa.B -0.51827 0.37492 0 0.16686
## nodematch.race..wa.H -0.23231 0.20782 0 0.26363
## nodematch.race..wa.O 0.50048 0.15516 0 0.00126 **
## absdiff.sqrt.age -0.56588 0.03254 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 7
summary(est.p.buildup.bal[[7]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + concurrent +
## nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") +
## degrange(from = 3) + offset(nodematch("role.class", diff = TRUE,
## keep = 1:2))
## <environment: 0x55babff89fe0>
##
## Iterations: 89 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.54354 0.16846 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.11233 0.02875 0 < 1e-04 ***
## nodefactor.race..wa.B 0.56471 0.13504 0 < 1e-04 ***
## nodefactor.race..wa.H 0.76359 0.14550 0 < 1e-04 ***
## nodefactor.region.EW -0.18174 0.04967 0 0.000253 ***
## nodefactor.region.OW -0.15146 0.03175 0 < 1e-04 ***
## concurrent 2.49735 0.06359 0 < 1e-04 ***
## nodematch.race..wa.B -0.52043 0.37590 0 0.166211
## nodematch.race..wa.H -0.23235 0.20670 0 0.260981
## nodematch.race..wa.O 0.50042 0.15633 0 0.001369 **
## absdiff.sqrt.age -0.54168 0.03245 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 8
summary(est.p.buildup.bal[[8]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + concurrent +
## nodematch("race..wa", diff = TRUE) + nodematch("region",
## diff = FALSE) + absdiff("sqrt.age") + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55bade5e6250>
##
## Iterations: 82 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -13.03488 0.17484 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.11231 0.02877 0 < 1e-04 ***
## nodefactor.race..wa.B 0.59283 0.13447 0 < 1e-04 ***
## nodefactor.race..wa.H 0.80083 0.14493 0 < 1e-04 ***
## nodefactor.region.EW 0.52492 0.04078 0 < 1e-04 ***
## nodefactor.region.OW 0.14806 0.02260 0 < 1e-04 ***
## concurrent 2.49753 0.06334 0 < 1e-04 ***
## nodematch.race..wa.B -0.58084 0.37503 0 0.121433
## nodematch.race..wa.H -0.31948 0.20560 0 0.120206
## nodematch.race..wa.O 0.53075 0.15580 0 0.000658 ***
## nodematch.region 1.79933 0.05810 0 < 1e-04 ***
## absdiff.sqrt.age -0.54144 0.03277 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Network diagnostics
Model 1
(dx_pers1 <- netdx(est.p.buildup.bal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.5 2059.669 0.021 42.921
## nodefactor.deg.main.1 NA 1846.935 NA 48.715
## nodefactor.race..wa.B NA 253.799 NA 15.857
## nodefactor.race..wa.H NA 445.554 NA 21.142
## nodefactor.region.EW NA 415.736 NA 20.061
## nodefactor.region.OW NA 1347.160 NA 38.175
## concurrent NA 628.469 NA 29.127
## nodematch.race..wa.B NA 7.826 NA 2.652
## nodematch.race..wa.H NA 24.191 NA 4.735
## nodematch.race..wa.O NA 1419.178 NA 34.707
## nodematch.region NA 915.952 NA 27.341
## absdiff.sqrt.age NA 2343.312 NA 60.823
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.663 -0.029 30.141
## Pct Edges Diss 0.032 0.032 -0.002 0.004plot(dx_pers1, type="formation")
plot(dx_pers1, type="duration")
plot(dx_pers1, type="dissolution")
Model 2
(dx_pers2 <- netdx(est.p.buildup.bal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2060.726 0.021 40.062
## nodefactor.deg.main.1 NA 1867.543 NA 47.468
## nodefactor.race..wa.B 285.517 290.728 0.018 15.759
## nodefactor.race..wa.H 605.340 616.057 0.018 24.265
## nodefactor.region.EW NA 435.364 NA 20.380
## nodefactor.region.OW NA 1335.239 NA 37.705
## concurrent NA 636.602 NA 26.910
## nodematch.race..wa.B NA 10.361 NA 3.159
## nodematch.race..wa.H NA 45.831 NA 6.540
## nodematch.race..wa.O NA 1254.134 NA 32.252
## nodematch.region NA 908.173 NA 28.712
## absdiff.sqrt.age NA 2346.623 NA 57.130
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.631 -0.030 30.105
## Pct Edges Diss 0.032 0.032 -0.002 0.004plot(dx_pers2, type="formation")
plot(dx_pers2, type="duration")
plot(dx_pers2, type="dissolution")
Model 3
(dx_pers3 <- netdx(est.p.buildup.bal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2055.890 0.019 40.837
## nodefactor.deg.main.1 NA 1854.729 NA 47.055
## nodefactor.race..wa.B 285.517 291.034 0.019 16.287
## nodefactor.race..wa.H 605.340 613.862 0.014 23.542
## nodefactor.region.EW NA 431.440 NA 19.738
## nodefactor.region.OW NA 1335.219 NA 39.201
## concurrent NA 633.098 NA 28.292
## nodematch.race..wa.B 8.480 8.407 -0.009 2.802
## nodematch.race..wa.H 51.181 51.494 0.006 6.920
## nodematch.race..wa.O 1247.081 1272.664 0.021 33.442
## nodematch.region NA 909.506 NA 27.461
## absdiff.sqrt.age NA 2342.499 NA 59.039
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.548 -0.032 30.049
## Pct Edges Diss 0.032 0.032 0.001 0.004plot(dx_pers3, type="formation")
plot(dx_pers3, type="duration")
plot(dx_pers3, type="dissolution")
Model 4
(dx_pers4 <- netdx(est.p.buildup.bal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2061.070 0.022 42.153
## nodefactor.deg.main.1 1699.000 1735.776 0.022 47.901
## nodefactor.race..wa.B 285.517 289.416 0.014 16.027
## nodefactor.race..wa.H 605.340 614.413 0.015 23.673
## nodefactor.region.EW NA 431.503 NA 20.768
## nodefactor.region.OW NA 1346.195 NA 40.288
## concurrent NA 637.361 NA 29.811
## nodematch.race..wa.B 8.480 8.554 0.009 3.069
## nodematch.race..wa.H 51.181 51.323 0.003 6.948
## nodematch.race..wa.O 1247.081 1278.867 0.025 33.885
## nodematch.region NA 908.921 NA 27.929
## absdiff.sqrt.age NA 2346.743 NA 63.112
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.628 -0.030 30.099
## Pct Edges Diss 0.032 0.032 -0.002 0.004plot(dx_pers4, type="formation")
plot(dx_pers4, type="duration")
plot(dx_pers4, type="dissolution")
Model 5
(dx_pers5 <- netdx(est.p.buildup.bal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2055.782 0.019 41.873
## nodefactor.deg.main.1 1699.000 1732.374 0.020 46.514
## nodefactor.race..wa.B 285.517 289.460 0.014 15.342
## nodefactor.race..wa.H 605.340 615.952 0.018 24.579
## nodefactor.region.EW 367.588 376.035 0.023 19.451
## nodefactor.region.OW 1182.255 1202.559 0.017 37.526
## concurrent NA 637.673 NA 29.160
## nodematch.race..wa.B 8.480 8.644 0.019 2.783
## nodematch.race..wa.H 51.181 52.035 0.017 6.983
## nodematch.race..wa.O 1247.081 1271.981 0.020 35.816
## nodematch.region NA 971.115 NA 30.757
## absdiff.sqrt.age NA 2340.434 NA 59.009
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.621 -0.030 30.086
## Pct Edges Diss 0.032 0.032 -0.001 0.004plot(dx_pers5, type="formation")
plot(dx_pers5, type="duration")
plot(dx_pers5, type="dissolution")
Model 6
(dx_pers6 <- netdx(est.p.buildup.bal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2050.562 0.016 40.344
## nodefactor.deg.main.1 1699.000 1726.555 0.016 46.411
## nodefactor.race..wa.B 285.517 288.219 0.009 16.881
## nodefactor.race..wa.H 605.340 611.583 0.010 23.079
## nodefactor.region.EW 367.588 374.534 0.019 18.886
## nodefactor.region.OW 1182.255 1202.498 0.017 36.476
## concurrent NA 640.420 NA 28.098
## nodematch.race..wa.B 8.480 8.793 0.037 2.977
## nodematch.race..wa.H 51.181 51.542 0.007 6.957
## nodematch.race..wa.O 1247.081 1271.470 0.020 34.569
## nodematch.region NA 967.810 NA 28.322
## absdiff.sqrt.age 1664.841 1687.014 0.013 45.283
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.547 -0.032 30.074
## Pct Edges Diss 0.032 0.032 0.001 0.004plot(dx_pers6, type="formation")
plot(dx_pers6, type="duration")
plot(dx_pers6, type="dissolution")
Model 7
(dx_pers7 <- netdx(est.p.buildup.bal[[7]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2070.715 0.026 65.110
## nodefactor.deg.main.1 1699.000 1763.286 0.038 66.290
## nodefactor.race..wa.B 285.517 286.895 0.005 19.654
## nodefactor.race..wa.H 605.340 596.049 -0.015 29.406
## nodefactor.region.EW 367.588 383.522 0.043 23.015
## nodefactor.region.OW 1182.255 1229.035 0.040 51.954
## concurrent 1384.000 1344.087 -0.029 56.113
## nodematch.race..wa.B 8.480 9.025 0.064 3.168
## nodematch.race..wa.H 51.181 46.022 -0.101 6.456
## nodematch.race..wa.O 1247.081 1296.576 0.040 47.250
## nodematch.region NA 973.100 NA 39.374
## absdiff.sqrt.age 1664.841 1785.756 0.073 69.978
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.645 -0.029 30.074
## Pct Edges Diss 0.032 0.032 -0.002 0.004plot(dx_pers7, type="formation")
plot(dx_pers7, type="duration")
plot(dx_pers7, type="dissolution")
Model 8
(dx_pers8 <- netdx(est.p.buildup.bal[[8]], nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.max = 1e+7)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2005.237 -0.006 62.821
## nodefactor.deg.main.1 1699.000 1706.711 0.005 60.760
## nodefactor.race..wa.B 285.517 280.520 -0.018 19.426
## nodefactor.race..wa.H 605.340 567.622 -0.062 27.334
## nodefactor.region.EW 367.588 347.872 -0.054 25.706
## nodefactor.region.OW 1182.255 1171.411 -0.009 60.295
## concurrent 1384.000 1266.774 -0.085 54.394
## nodematch.race..wa.B 8.480 8.993 0.061 3.301
## nodematch.race..wa.H 51.181 42.507 -0.169 6.479
## nodematch.race..wa.O 1247.081 1261.286 0.011 48.533
## nodematch.region 1614.000 1529.288 -0.052 51.823
## absdiff.sqrt.age 1664.841 1737.859 0.044 65.692
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.574 -0.032 30.040
## Pct Edges Diss 0.032 0.032 0.000 0.004plot(dx_pers8, type="formation")
plot(dx_pers8, type="duration")
plot(dx_pers8, type="dissolution")